diff --git a/examples/notebooks/jiant_Basic_Example.ipynb b/examples/notebooks/jiant_Basic_Example.ipynb
index 5bcd74584..8571efb6a 100644
--- a/examples/notebooks/jiant_Basic_Example.ipynb
+++ b/examples/notebooks/jiant_Basic_Example.ipynb
@@ -85,6 +85,7 @@
"outputs": [],
"source": [
"%%capture\n",
+ "%cd /content\n",
"# Download MRPC data\n",
"!PYTHONPATH=/content/jiant python jiant/jiant/scripts/download_data/runscript.py \\\n",
" download \\\n",
@@ -309,8 +310,8 @@
" jiant_task_container_config_path=\"./run_configs/mrpc_run_config.json\",\n",
" output_dir=\"./runs/mrpc\",\n",
" hf_pretrained_model_name_or_path=\"roberta-base\",\n",
- " model_path=\"./models/roberta-base/model/roberta-base.p\",\n",
- " model_config_path=\"./models/roberta-base/model/roberta-base.json\",\n",
+ " model_path=\"./models/roberta-base/model/roberta.p\",\n",
+ " model_config_path=\"./models/roberta-base/model/roberta.json\",\n",
" learning_rate=1e-5,\n",
" eval_every_steps=500,\n",
" do_train=True,\n",
diff --git a/examples/notebooks/jiant_EdgeProbing_Example.ipynb b/examples/notebooks/jiant_EdgeProbing_Example.ipynb
index e3ffd5986..d04a370ec 100644
--- a/examples/notebooks/jiant_EdgeProbing_Example.ipynb
+++ b/examples/notebooks/jiant_EdgeProbing_Example.ipynb
@@ -1,3496 +1,3496 @@
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+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "O9I9rz0pTamX"
+ },
+ "source": [
+ "# Edge-Probing Fine-tuning Example"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "EiowR0WNTd1C"
+ },
+ "source": [
+ "In this notebook, we will:\n",
+ "\n",
+ "* Train a RoBERTa base model on Edge-Probing (Semeval) and evaluate its performance\n",
+ "* Because the Edge-Probing data is not publicly available, we will simulate the run with a single example. This will serve as a guide for users who have access to the task data, or similarly formatted data.\n",
+ "* **The encoder is not frozen for training runs in this notebook.**\n",
+ "\n",
+ "The code shown in this notebook will work, but the results will not be representative of the task!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rXbD_U1_VDnw"
+ },
+ "source": [
+ "## Setup"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "tC9teoazUnW8"
+ },
+ "source": [
+ "#### Install dependencies\n",
+ "\n",
+ "First, we will install libraries we need for this code."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "8aU3Z9szuMU9"
+ },
+ "source": [
+ "%%capture\n",
+ "!git clone https://github.com/nyu-mll/jiant.git\n",
+ "%cd jiant\n",
+ "!pip install -r requirements-no-torch.txt\n",
+ "!pip install --no-deps -e ./\n",
+ "%cd .."
+ ],
+ "execution_count": 1,
+ "outputs": []
},
- "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rQKSAhYzVIlv"
+ },
+ "source": [
+ "## `jiant` Pipeline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "v88oXqmBvFuK"
+ },
+ "source": [
+ "import sys\n",
+ "sys.path.insert(0, \"/content/jiant\")"
+ ],
+ "execution_count": 2,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ibmMT7CXv1_P"
+ },
+ "source": [
+ "import jiant.proj.main.tokenize_and_cache as tokenize_and_cache\n",
+ "import jiant.proj.main.export_model as export_model\n",
+ "import jiant.proj.main.scripts.configurator as configurator\n",
+ "import jiant.proj.main.runscript as main_runscript\n",
+ "import jiant.shared.caching as caching\n",
+ "import jiant.utils.python.io as py_io\n",
+ "import jiant.utils.display as display\n",
+ "import os"
+ ],
+ "execution_count": 3,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-ihjR1g_1phl"
+ },
+ "source": [
+ "## Creating sample Edge-Probing data.\n",
+ "\n",
+ "Because the Edge-Probing data is not publicly available, we will simulate the run with a single example. We will write 1000 copies for the training set and 100 copies for the validation set. We will also write the corresponding task config."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "jKCz8VksvFlN"
+ },
+ "source": [
+ "example = {\n",
+ " \"text\": \"The current view is that the chronic inflammation in the distal part of the stomach caused by Helicobacter pylori infection results in an increased acid production from the non-infected upper corpus region of the stomach.\",\n",
+ " \"info\": {\"id\": 7},\n",
+ " \"targets\": [\n",
+ " {\n",
+ " \"label\": \"Cause-Effect(e2,e1)\",\n",
+ " \"span1\": [7,8],\n",
+ " \"span2\": [19, 20],\n",
+ " \"info\": {\"comment\": \"\"}\n",
+ " }\n",
+ " ]\n",
+ "}\n",
+ "# Simulate a training set of 1000 examples\n",
+ "train_data = [example] * 1000\n",
+ "# Simulate a validation set of 100 examples\n",
+ "val_data = [example] * 100"
+ ],
+ "execution_count": 4,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Uvt8Zi86yHHa"
+ },
+ "source": [
+ "os.makedirs(\"/content/tasks/configs/\", exist_ok=True)\n",
+ "os.makedirs(\"/content/tasks/data/semeval\", exist_ok=True)\n",
+ "py_io.write_jsonl(\n",
+ " data=train_data,\n",
+ " path=\"/content/tasks/data/semeval/train.jsonl\",\n",
+ ")\n",
+ "py_io.write_jsonl(\n",
+ " data=val_data,\n",
+ " path=\"/content/tasks/data/semeval/val.jsonl\",\n",
+ ")\n",
+ "py_io.write_json({\n",
+ " \"task\": \"semeval\",\n",
+ " \"paths\": {\n",
+ " \"train\": \"/content/tasks/data/semeval/train.jsonl\",\n",
+ " \"val\": \"/content/tasks/data/semeval/val.jsonl\",\n",
+ " },\n",
+ " \"name\": \"semeval\"\n",
+ "}, \"/content/tasks/configs/semeval_config.json\")"
+ ],
+ "execution_count": 5,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HPZHyLOlVp07"
+ },
+ "source": [
+ "#### Download model\n",
+ "\n",
+ "Next, we will download a `roberta-base` model. This also includes the tokenizer."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 269,
+ "referenced_widgets": [
+ "0ad0c4ef8cc64749b6bd2ccb2ba41563",
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+ ]
+ },
+ "id": "K06qUGjkKWa7",
+ "outputId": "c21bdffa-0ff3-49f3-e734-af5530ab4711"
+ },
+ "source": [
+ "export_model.export_model(\n",
+ " hf_pretrained_model_name_or_path=\"roberta-base\",\n",
+ " output_base_path=\"./models/roberta-base\",\n",
+ ")"
+ ],
+ "execution_count": 6,
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {
- "id": "O9I9rz0pTamX"
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "0ad0c4ef8cc64749b6bd2ccb2ba41563",
+ "version_minor": 0,
+ "version_major": 2
},
- "source": [
- "# Edge-Probing Fine-tuning Example"
+ "text/plain": [
+ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=481.0, style=ProgressStyle(description_…"
]
+ },
+ "metadata": {
+ "tags": []
+ }
},
{
- "cell_type": "markdown",
- "metadata": {
- "id": "EiowR0WNTd1C"
- },
- "source": [
- "In this notebook, we will:\n",
- "\n",
- "* Train a RoBERTa base model on Edge-Probing (Semeval) and evaluate its performance\n",
- "* Because the Edge-Probing data is not publicly available, we will simulate the run with a single example. This will serve as a guide for users who have access to the task data, or similarly formatted data.\n",
- "* **The encoder is not frozen for training runs in this notebook.**\n",
- "\n",
- "The code shown in this notebook will work, but the results will not be representative of the task!"
- ]
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ],
+ "name": "stdout"
},
{
- "cell_type": "markdown",
- "metadata": {
- "id": "rXbD_U1_VDnw"
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "273d939ae19a47ae976c3a7afe9403b8",
+ "version_minor": 0,
+ "version_major": 2
},
- "source": [
- "## Setup"
+ "text/plain": [
+ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=501200538.0, style=ProgressStyle(descri…"
]
+ },
+ "metadata": {
+ "tags": []
+ }
+ },
+ {
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "stream",
+ "text": [
+ "Some weights of RobertaForMaskedLM were not initialized from the model checkpoint at roberta-base and are newly initialized: ['lm_head.decoder.bias']\n",
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+ ],
+ "name": "stderr"
},
{
- "cell_type": "markdown",
- "metadata": {
- "id": "tC9teoazUnW8"
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "cba1747a59364cab89af52f64d7d2be4",
+ "version_minor": 0,
+ "version_major": 2
},
- "source": [
- "#### Install dependencies\n",
- "\n",
- "First, we will install libraries we need for this code."
+ "text/plain": [
+ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=898823.0, style=ProgressStyle(descripti…"
]
+ },
+ "metadata": {
+ "tags": []
+ }
},
{
- "cell_type": "code",
- "metadata": {
- "id": "8aU3Z9szuMU9"
- },
- "source": [
- "%%capture\n",
- "!git clone https://github.com/nyu-mll/jiant.git\n",
- "%cd jiant\n",
- "!pip install -r requirements-no-torch.txt\n",
- "!pip install --no-deps -e ./\n",
- "%cd .."
- ],
- "execution_count": 1,
- "outputs": []
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ],
+ "name": "stdout"
},
{
- "cell_type": "markdown",
- "metadata": {
- "id": "rQKSAhYzVIlv"
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "c795cd64b082451f9876de86ea6353ea",
+ "version_minor": 0,
+ "version_major": 2
},
- "source": [
- "## `jiant` Pipeline"
+ "text/plain": [
+ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=456318.0, style=ProgressStyle(descripti…"
]
+ },
+ "metadata": {
+ "tags": []
+ }
},
{
- "cell_type": "code",
- "metadata": {
- "id": "v88oXqmBvFuK"
- },
- "source": [
- "import sys\n",
- "sys.path.insert(0, \"/content/jiant\")"
- ],
- "execution_count": 2,
- "outputs": []
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dV-T-8r1V0wf"
+ },
+ "source": [
+ "#### Tokenize and cache\n",
+ "\n",
+ "With the model and data ready, we can now tokenize and cache the inputs features for our task. This converts the input examples to tokenized features ready to be consumed by the model, and saved them to disk in chunks."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 168,
+ "referenced_widgets": [
+ "558652d65c9c42e5b2487711ea8c5183",
+ "b59db489de7a410fb16ac4c64554c2db",
+ "648bd87cbe344ce786e78d902456be01",
+ "763685078bfa425a84652935a1d49965",
+ "5cada33c5612410482ab58422f866621",
+ "635bed807450413797ec7d1c8a16444d",
+ "9878a78abe8e41c585f63ed1e77309ea",
+ "2b0ee8fa0e614926b8ea2befa8d93893",
+ "6bfcf6f5ebc144e88f55e1501c398ab3",
+ "0fe088ff545b4642993a0395bc0351af",
+ "6644d495d3ab4198962cc3e4fd130fe0",
+ "95e0eeb66af842fc9a5ea7072e665045",
+ "13039f3a86204fdea19dd335aaa66519",
+ "effe903b59b24573b1c3e406b986dfac",
+ "b6aeddb44c3147e2ab912d84759bc139",
+ "1b51de8cdcbd4c01af4de5865e9c575d"
+ ]
},
+ "id": "22bNWQajO4zm",
+ "outputId": "a8cf3ed5-c86f-42aa-9a20-c9e97dc51998"
+ },
+ "source": [
+ "# Tokenize and cache each task\n",
+ "task_name = \"semeval\"\n",
+ "\n",
+ "tokenize_and_cache.main(tokenize_and_cache.RunConfiguration(\n",
+ " task_config_path=f\"./tasks/configs/{task_name}_config.json\",\n",
+ " hf_pretrained_model_name_or_path=\"roberta-base\",\n",
+ " output_dir=f\"./cache/{task_name}\",\n",
+ " phases=[\"train\", \"val\"],\n",
+ "))"
+ ],
+ "execution_count": 7,
+ "outputs": [
{
- "cell_type": "code",
- "metadata": {
- "id": "ibmMT7CXv1_P"
- },
- "source": [
- "import jiant.proj.main.tokenize_and_cache as tokenize_and_cache\n",
- "import jiant.proj.main.export_model as export_model\n",
- "import jiant.proj.main.scripts.configurator as configurator\n",
- "import jiant.proj.main.runscript as main_runscript\n",
- "import jiant.shared.caching as caching\n",
- "import jiant.utils.python.io as py_io\n",
- "import jiant.utils.display as display\n",
- "import os"
- ],
- "execution_count": 3,
- "outputs": []
+ "output_type": "stream",
+ "text": [
+ "SemevalTask\n",
+ " [train]: /content/tasks/data/semeval/train.jsonl\n",
+ " [val]: /content/tasks/data/semeval/val.jsonl\n"
+ ],
+ "name": "stdout"
},
{
- "cell_type": "markdown",
- "metadata": {
- "id": "-ihjR1g_1phl"
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "558652d65c9c42e5b2487711ea8c5183",
+ "version_minor": 0,
+ "version_major": 2
},
- "source": [
- "## Creating sample Edge-Probing data.\n",
- "\n",
- "Because the Edge-Probing data is not publicly available, we will simulate the run with a single example. We will write 1000 copies for the training set and 100 copies for the validation set. We will also write the corresponding task config."
+ "text/plain": [
+ "HBox(children=(FloatProgress(value=0.0, description='Tokenizing', max=1000.0, style=ProgressStyle(description_…"
]
+ },
+ "metadata": {
+ "tags": []
+ }
},
{
- "cell_type": "code",
- "metadata": {
- "id": "jKCz8VksvFlN"
- },
- "source": [
- "example = {\n",
- " \"text\": \"The current view is that the chronic inflammation in the distal part of the stomach caused by Helicobacter pylori infection results in an increased acid production from the non-infected upper corpus region of the stomach.\",\n",
- " \"info\": {\"id\": 7},\n",
- " \"targets\": [\n",
- " {\n",
- " \"label\": \"Cause-Effect(e2,e1)\",\n",
- " \"span1\": [7,8],\n",
- " \"span2\": [19, 20],\n",
- " \"info\": {\"comment\": \"\"}\n",
- " }\n",
- " ]\n",
- "}\n",
- "# Simulate a training set of 1000 examples\n",
- "train_data = [example] * 1000\n",
- "# Simulate a validation set of 100 examples\n",
- "val_data = [example] * 100"
- ],
- "execution_count": 4,
- "outputs": []
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ],
+ "name": "stdout"
},
{
- "cell_type": "code",
- "metadata": {
- "id": "Uvt8Zi86yHHa"
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "6bfcf6f5ebc144e88f55e1501c398ab3",
+ "version_minor": 0,
+ "version_major": 2
},
- "source": [
- "os.makedirs(\"/content/tasks/configs/\", exist_ok=True)\n",
- "os.makedirs(\"/content/tasks/data/semeval\", exist_ok=True)\n",
- "py_io.write_jsonl(\n",
- " data=train_data,\n",
- " path=\"/content/tasks/data/semeval/train.jsonl\",\n",
- ")\n",
- "py_io.write_jsonl(\n",
- " data=val_data,\n",
- " path=\"/content/tasks/data/semeval/val.jsonl\",\n",
- ")\n",
- "py_io.write_json({\n",
- " \"task\": \"semeval\",\n",
- " \"paths\": {\n",
- " \"train\": \"/content/tasks/data/semeval/train.jsonl\",\n",
- " \"val\": \"/content/tasks/data/semeval/val.jsonl\",\n",
- " },\n",
- " \"name\": \"semeval\"\n",
- "}, \"/content/tasks/configs/semeval_config.json\")"
- ],
- "execution_count": 5,
- "outputs": []
+ "text/plain": [
+ "HBox(children=(FloatProgress(value=0.0, description='Tokenizing', style=ProgressStyle(description_width='initi…"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
},
{
- "cell_type": "markdown",
- "metadata": {
- "id": "HPZHyLOlVp07"
- },
- "source": [
- "#### Download model\n",
- "\n",
- "Next, we will download a `roberta-base` model. This also includes the tokenizer."
- ]
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JJ-mWSQQWJsw"
+ },
+ "source": [
+ "We can inspect the first examples of the first chunk of each task."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "iLk_X0KypUyr",
+ "outputId": "5779503a-c60e-4d51-e587-caabb11815ff"
+ },
+ "source": [
+ "row = caching.ChunkedFilesDataCache(\"./cache/semeval/train\").load_chunk(0)[0][\"data_row\"]\n",
+ "print(row.input_ids)\n",
+ "print(row.tokens)\n",
+ "print(row.tokens[row.spans[0][0]: row.spans[0][1]+1])\n",
+ "print(row.tokens[row.spans[1][0]: row.spans[1][1]+1])"
+ ],
+ "execution_count": 14,
+ "outputs": [
{
- "cell_type": "code",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 269,
- "referenced_widgets": [
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- ]
- },
- "id": "K06qUGjkKWa7",
- "outputId": "c21bdffa-0ff3-49f3-e734-af5530ab4711"
- },
- "source": [
- "export_model.export_model(\n",
- " hf_pretrained_model_name_or_path=\"roberta-base\",\n",
- " output_base_path=\"./models/roberta-base\",\n",
- ")"
- ],
- "execution_count": 6,
- "outputs": [
- {
- "output_type": "display_data",
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "0ad0c4ef8cc64749b6bd2ccb2ba41563",
- "version_minor": 0,
- "version_major": 2
- },
- "text/plain": [
- "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=481.0, style=ProgressStyle(description_…"
- ]
- },
- "metadata": {
- "tags": []
- }
- },
- {
- "output_type": "stream",
- "text": [
- "\n"
- ],
- "name": "stdout"
- },
- {
- "output_type": "display_data",
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "273d939ae19a47ae976c3a7afe9403b8",
- "version_minor": 0,
- "version_major": 2
- },
- "text/plain": [
- "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=501200538.0, style=ProgressStyle(descri…"
- ]
- },
- "metadata": {
- "tags": []
- }
- },
- {
- "output_type": "stream",
- "text": [
- "\n"
- ],
- "name": "stdout"
- },
- {
- "output_type": "stream",
- "text": [
- "Some weights of RobertaForMaskedLM were not initialized from the model checkpoint at roberta-base and are newly initialized: ['lm_head.decoder.bias']\n",
- "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
- ],
- "name": "stderr"
- },
- {
- "output_type": "display_data",
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "cba1747a59364cab89af52f64d7d2be4",
- "version_minor": 0,
- "version_major": 2
- },
- "text/plain": [
- "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=898823.0, style=ProgressStyle(descripti…"
- ]
- },
- "metadata": {
- "tags": []
- }
- },
- {
- "output_type": "stream",
- "text": [
- "\n"
- ],
- "name": "stdout"
- },
- {
- "output_type": "display_data",
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "c795cd64b082451f9876de86ea6353ea",
- "version_minor": 0,
- "version_major": 2
- },
- "text/plain": [
- "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=456318.0, style=ProgressStyle(descripti…"
- ]
- },
- "metadata": {
- "tags": []
- }
- },
- {
- "output_type": "stream",
- "text": [
- "\n"
- ],
- "name": "stdout"
- }
- ]
+ "output_type": "stream",
+ "text": [
+ "[ 0 133 595 1217 16 14 5 7642 16000 11 5 7018\n",
+ " 337 233 9 5 9377 1726 30 31141 2413 35995 181 4360\n",
+ " 6249 7910 775 11 41 1130 10395 931 31 5 786 12\n",
+ " 37597 196 2853 42168 976 9 5 9377 4 2 1 1\n",
+ " 1 1 1 1 1 1 1 1 1 1 1 1\n",
+ " 1 1 1 1 1 1 1 1 1 1 1 1\n",
+ " 1 1 1 1 1 1 1 1 1 1 1 1\n",
+ " 1 1 1 1 1 1 1 1 1 1 1 1\n",
+ " 1 1 1 1 1 1 1 1 1 1 1 1\n",
+ " 1 1 1 1 1 1 1 1 1 1 1 1\n",
+ " 1 1 1 1 1 1 1 1]\n",
+ "['', 'The', 'Ġcurrent', 'Ġview', 'Ġis', 'Ġthat', 'Ġthe', 'Ġchronic', 'Ġinflammation', 'Ġin', 'Ġthe', 'Ġdist', 'al', 'Ġpart', 'Ġof', 'Ġthe', 'Ġstomach', 'Ġcaused', 'Ġby', 'ĠHelic', 'ob', 'acter', 'Ġp', 'yl', 'ori', 'Ġinfection', 'Ġresults', 'Ġin', 'Ġan', 'Ġincreased', 'Ġacid', 'Ġproduction', 'Ġfrom', 'Ġthe', 'Ġnon', '-', 'infect', 'ed', 'Ġupper', 'Ġcorpus', 'Ġregion', 'Ġof', 'Ġthe', 'Ġstomach', '.', '']\n",
+ "['Ġinflammation']\n",
+ "['Ġinfection']\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3MBuH19IWOr0"
+ },
+ "source": [
+ "#### Writing a run config\n",
+ "\n",
+ "Here we are going to write what we call a `jiant_task_container_config`. This configuration file basically defines a lot of the subtleties of our training pipeline, such as what tasks we will train on, do evaluation on, batch size for each task. The new version of `jiant` leans heavily toward explicitly specifying everything, for the purpose of inspectability and leaving minimal surprises for the user, even as the cost of being more verbose.\n",
+ "\n",
+ "We use a helper \"Configurator\" to write out a `jiant_task_container_config`, since most of our setup is pretty standard. \n",
+ "\n",
+ "**Depending on what GPU your Colab session is assigned to, you may need to lower the train batch size.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "pQYtl7xTKsiP",
+ "outputId": "00d58dc6-3d0d-40fa-8a3f-d19553803567"
+ },
+ "source": [
+ "jiant_run_config = configurator.SimpleAPIMultiTaskConfigurator(\n",
+ " task_config_base_path=\"./tasks/configs\",\n",
+ " task_cache_base_path=\"./cache\",\n",
+ " train_task_name_list=[\"semeval\"],\n",
+ " val_task_name_list=[\"semeval\"],\n",
+ " train_batch_size=8,\n",
+ " eval_batch_size=16,\n",
+ " epochs=3,\n",
+ " num_gpus=1,\n",
+ ").create_config()\n",
+ "os.makedirs(\"./run_configs/\", exist_ok=True)\n",
+ "py_io.write_json(jiant_run_config, \"./run_configs/semeval_run_config.json\")\n",
+ "display.show_json(jiant_run_config)"
+ ],
+ "execution_count": 15,
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {
- "id": "dV-T-8r1V0wf"
- },
- "source": [
- "#### Tokenize and cache\n",
- "\n",
- "With the model and data ready, we can now tokenize and cache the inputs features for our task. This converts the input examples to tokenized features ready to be consumed by the model, and saved them to disk in chunks."
- ]
+ "output_type": "stream",
+ "text": [
+ "{\n",
+ " \"task_config_path_dict\": {\n",
+ " \"semeval\": \"./tasks/configs/semeval_config.json\"\n",
+ " },\n",
+ " \"task_cache_config_dict\": {\n",
+ " \"semeval\": {\n",
+ " \"train\": \"./cache/semeval/train\",\n",
+ " \"val\": \"./cache/semeval/val\",\n",
+ " \"val_labels\": \"./cache/semeval/val_labels\"\n",
+ " }\n",
+ " },\n",
+ " \"sampler_config\": {\n",
+ " \"sampler_type\": \"ProportionalMultiTaskSampler\"\n",
+ " },\n",
+ " \"global_train_config\": {\n",
+ " \"max_steps\": 375,\n",
+ " \"warmup_steps\": 37\n",
+ " },\n",
+ " \"task_specific_configs_dict\": {\n",
+ " \"semeval\": {\n",
+ " \"train_batch_size\": 8,\n",
+ " \"eval_batch_size\": 16,\n",
+ " \"gradient_accumulation_steps\": 1,\n",
+ " \"eval_subset_num\": 500\n",
+ " }\n",
+ " },\n",
+ " \"taskmodels_config\": {\n",
+ " \"task_to_taskmodel_map\": {\n",
+ " \"semeval\": \"semeval\"\n",
+ " },\n",
+ " \"taskmodel_config_map\": {\n",
+ " \"semeval\": null\n",
+ " }\n",
+ " },\n",
+ " \"task_run_config\": {\n",
+ " \"train_task_list\": [\n",
+ " \"semeval\"\n",
+ " ],\n",
+ " \"train_val_task_list\": [\n",
+ " \"semeval\"\n",
+ " ],\n",
+ " \"val_task_list\": [\n",
+ " \"semeval\"\n",
+ " ],\n",
+ " \"test_task_list\": []\n",
+ " },\n",
+ " \"metric_aggregator_config\": {\n",
+ " \"metric_aggregator_type\": \"EqualMetricAggregator\"\n",
+ " }\n",
+ "}\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-UF501yoXHBi"
+ },
+ "source": [
+ "To briefly go over the major components of the `jiant_task_container_config`:\n",
+ "\n",
+ "* `task_config_path_dict`: The paths to the task config files we wrote above.\n",
+ "* `task_cache_config_dict`: The paths to the task features caches we generated above.\n",
+ "* `sampler_config`: Determines how to sample from different tasks during training.\n",
+ "* `global_train_config`: The number of total steps and warmup steps during training.\n",
+ "* `task_specific_configs_dict`: Task-specific arguments for each task, such as training batch size and gradient accumulation steps.\n",
+ "* `taskmodels_config`: Task-model specific arguments for each task-model, including what tasks use which model.\n",
+ "* `metric_aggregator_config`: Determines how to weight/aggregate the metrics across multiple tasks."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BBKkvXzdYPqZ"
+ },
+ "source": [
+ "#### Start training\n",
+ "\n",
+ "Finally, we can start our training run. \n",
+ "\n",
+ "Before starting training, the script also prints out the list of parameters in our model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000,
+ "referenced_widgets": [
+ "f081100984b44e45a77fff620f998508",
+ "69d9699a4d7147b5bf9ada54c4839488",
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+ ]
},
+ "id": "JdwWPgjQWx6I",
+ "outputId": "ba7e86d3-76e4-47bc-b61a-188783502323"
+ },
+ "source": [
+ "run_args = main_runscript.RunConfiguration(\n",
+ " jiant_task_container_config_path=\"./run_configs/semeval_run_config.json\",\n",
+ " output_dir=\"./runs/semeval\",\n",
+ " hf_pretrained_model_name_or_path=\"roberta-base\",\n",
+ " model_path=\"./models/roberta-base/model/roberta.p\",\n",
+ " model_config_path=\"./models/roberta-base/model/roberta.json\",\n",
+ " learning_rate=1e-5,\n",
+ " eval_every_steps=500,\n",
+ " do_train=True,\n",
+ " do_val=True,\n",
+ " do_save=True,\n",
+ " force_overwrite=True,\n",
+ ")\n",
+ "main_runscript.run_loop(run_args)"
+ ],
+ "execution_count": 16,
+ "outputs": [
{
- "cell_type": "code",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 168,
- "referenced_widgets": [
- "558652d65c9c42e5b2487711ea8c5183",
- "b59db489de7a410fb16ac4c64554c2db",
- "648bd87cbe344ce786e78d902456be01",
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- ]
- },
- "id": "22bNWQajO4zm",
- "outputId": "a8cf3ed5-c86f-42aa-9a20-c9e97dc51998"
- },
- "source": [
- "# Tokenize and cache each task\n",
- "task_name = \"semeval\"\n",
- "\n",
- "tokenize_and_cache.main(tokenize_and_cache.RunConfiguration(\n",
- " task_config_path=f\"./tasks/configs/{task_name}_config.json\",\n",
- " hf_pretrained_model_name_or_path=\"roberta-base\",\n",
- " output_dir=f\"./cache/{task_name}\",\n",
- " phases=[\"train\", \"val\"],\n",
- "))"
- ],
- "execution_count": 7,
- "outputs": [
- {
- "output_type": "stream",
- "text": [
- "SemevalTask\n",
- " [train]: /content/tasks/data/semeval/train.jsonl\n",
- " [val]: /content/tasks/data/semeval/val.jsonl\n"
- ],
- "name": "stdout"
- },
- {
- "output_type": "display_data",
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "558652d65c9c42e5b2487711ea8c5183",
- "version_minor": 0,
- "version_major": 2
- },
- "text/plain": [
- "HBox(children=(FloatProgress(value=0.0, description='Tokenizing', max=1000.0, style=ProgressStyle(description_…"
- ]
- },
- "metadata": {
- "tags": []
- }
- },
- {
- "output_type": "stream",
- "text": [
- "\n"
- ],
- "name": "stdout"
- },
- {
- "output_type": "display_data",
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "6bfcf6f5ebc144e88f55e1501c398ab3",
- "version_minor": 0,
- "version_major": 2
- },
- "text/plain": [
- "HBox(children=(FloatProgress(value=0.0, description='Tokenizing', style=ProgressStyle(description_width='initi…"
- ]
- },
- "metadata": {
- "tags": []
- }
- },
- {
- "output_type": "stream",
- "text": [
- "\n"
- ],
- "name": "stdout"
- }
- ]
+ "output_type": "stream",
+ "text": [
+ " jiant_task_container_config_path: ./run_configs/semeval_run_config.json\n",
+ " output_dir: ./runs/semeval\n",
+ " model_type: roberta-base\n",
+ " model_path: ./models/roberta-base/model/roberta-base.p\n",
+ " model_config_path: ./models/roberta-base/model/roberta-base.json\n",
+ " model_load_mode: from_transformers\n",
+ " do_train: True\n",
+ " do_val: True\n",
+ " do_save: True\n",
+ " do_save_last: False\n",
+ " do_save_best: False\n",
+ " write_val_preds: False\n",
+ " write_test_preds: False\n",
+ " eval_every_steps: 500\n",
+ " save_every_steps: 0\n",
+ " save_checkpoint_every_steps: 0\n",
+ " no_improvements_for_n_evals: 0\n",
+ " keep_checkpoint_when_done: False\n",
+ " force_overwrite: True\n",
+ " seed: -1\n",
+ " learning_rate: 1e-05\n",
+ " adam_epsilon: 1e-08\n",
+ " max_grad_norm: 1.0\n",
+ " optimizer_type: adam\n",
+ " no_cuda: False\n",
+ " fp16: False\n",
+ " fp16_opt_level: O1\n",
+ " local_rank: -1\n",
+ " server_ip: \n",
+ " server_port: \n",
+ "device: cuda n_gpu: 1, distributed training: False, 16-bits training: False\n",
+ "Using seed: 195818355\n",
+ "{\n",
+ " \"jiant_task_container_config_path\": \"./run_configs/semeval_run_config.json\",\n",
+ " \"output_dir\": \"./runs/semeval\",\n",
+ " \"model_type\": \"roberta-base\",\n",
+ " \"model_path\": \"./models/roberta-base/model/roberta-base.p\",\n",
+ " \"model_config_path\": \"./models/roberta-base/model/roberta-base.json\",\n",
+ " \"model_load_mode\": \"from_transformers\",\n",
+ " \"do_train\": true,\n",
+ " \"do_val\": true,\n",
+ " \"do_save\": true,\n",
+ " \"do_save_last\": false,\n",
+ " \"do_save_best\": false,\n",
+ " \"write_val_preds\": false,\n",
+ " \"write_test_preds\": false,\n",
+ " \"eval_every_steps\": 500,\n",
+ " \"save_every_steps\": 0,\n",
+ " \"save_checkpoint_every_steps\": 0,\n",
+ " \"no_improvements_for_n_evals\": 0,\n",
+ " \"keep_checkpoint_when_done\": false,\n",
+ " \"force_overwrite\": true,\n",
+ " \"seed\": 195818355,\n",
+ " \"learning_rate\": 1e-05,\n",
+ " \"adam_epsilon\": 1e-08,\n",
+ " \"max_grad_norm\": 1.0,\n",
+ " \"optimizer_type\": \"adam\",\n",
+ " \"no_cuda\": false,\n",
+ " \"fp16\": false,\n",
+ " \"fp16_opt_level\": \"O1\",\n",
+ " \"local_rank\": -1,\n",
+ " \"server_ip\": \"\",\n",
+ " \"server_port\": \"\"\n",
+ "}\n",
+ "1\n",
+ "Creating Tasks:\n",
+ " semeval (SemevalTask): ./tasks/configs/semeval_config.json\n"
+ ],
+ "name": "stdout"
},
{
- "cell_type": "markdown",
- "metadata": {
- "id": "JJ-mWSQQWJsw"
- },
- "source": [
- "We can inspect the first examples of the first chunk of each task."
- ]
+ "output_type": "stream",
+ "text": [
+ "/content/jiant/jiant/proj/main/components/container_setup.py:78: UserWarning: task semeval from ./tasks/configs/semeval_config.json has conflicting names: semeval/semval. Using semeval\n",
+ " task_name, task_config_path, task_name, task.name, task_name,\n"
+ ],
+ "name": "stderr"
},
{
- "cell_type": "code",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "iLk_X0KypUyr",
- "outputId": "5779503a-c60e-4d51-e587-caabb11815ff"
- },
- "source": [
- "row = caching.ChunkedFilesDataCache(\"./cache/semeval/train\").load_chunk(0)[0][\"data_row\"]\n",
- "print(row.input_ids)\n",
- "print(row.tokens)\n",
- "print(row.tokens[row.spans[0][0]: row.spans[0][1]+1])\n",
- "print(row.tokens[row.spans[1][0]: row.spans[1][1]+1])"
- ],
- "execution_count": 14,
- "outputs": [
- {
- "output_type": "stream",
- "text": [
- "[ 0 133 595 1217 16 14 5 7642 16000 11 5 7018\n",
- " 337 233 9 5 9377 1726 30 31141 2413 35995 181 4360\n",
- " 6249 7910 775 11 41 1130 10395 931 31 5 786 12\n",
- " 37597 196 2853 42168 976 9 5 9377 4 2 1 1\n",
- " 1 1 1 1 1 1 1 1 1 1 1 1\n",
- " 1 1 1 1 1 1 1 1 1 1 1 1\n",
- " 1 1 1 1 1 1 1 1 1 1 1 1\n",
- " 1 1 1 1 1 1 1 1 1 1 1 1\n",
- " 1 1 1 1 1 1 1 1 1 1 1 1\n",
- " 1 1 1 1 1 1 1 1 1 1 1 1\n",
- " 1 1 1 1 1 1 1 1]\n",
- "['', 'The', 'Ġcurrent', 'Ġview', 'Ġis', 'Ġthat', 'Ġthe', 'Ġchronic', 'Ġinflammation', 'Ġin', 'Ġthe', 'Ġdist', 'al', 'Ġpart', 'Ġof', 'Ġthe', 'Ġstomach', 'Ġcaused', 'Ġby', 'ĠHelic', 'ob', 'acter', 'Ġp', 'yl', 'ori', 'Ġinfection', 'Ġresults', 'Ġin', 'Ġan', 'Ġincreased', 'Ġacid', 'Ġproduction', 'Ġfrom', 'Ġthe', 'Ġnon', '-', 'infect', 'ed', 'Ġupper', 'Ġcorpus', 'Ġregion', 'Ġof', 'Ġthe', 'Ġstomach', '.', '']\n",
- "['Ġinflammation']\n",
- "['Ġinfection']\n"
- ],
- "name": "stdout"
- }
- ]
+ "output_type": "stream",
+ "text": [
+ "No optimizer decay for:\n",
+ " encoder.embeddings.LayerNorm.weight\n",
+ " encoder.embeddings.LayerNorm.bias\n",
+ " encoder.encoder.layer.0.attention.self.query.bias\n",
+ " encoder.encoder.layer.0.attention.self.key.bias\n",
+ " encoder.encoder.layer.0.attention.self.value.bias\n",
+ " encoder.encoder.layer.0.attention.output.dense.bias\n",
+ " encoder.encoder.layer.0.attention.output.LayerNorm.weight\n",
+ " encoder.encoder.layer.0.attention.output.LayerNorm.bias\n",
+ " encoder.encoder.layer.0.intermediate.dense.bias\n",
+ " encoder.encoder.layer.0.output.dense.bias\n",
+ " encoder.encoder.layer.0.output.LayerNorm.weight\n",
+ " encoder.encoder.layer.0.output.LayerNorm.bias\n",
+ " encoder.encoder.layer.1.attention.self.query.bias\n",
+ " encoder.encoder.layer.1.attention.self.key.bias\n",
+ " encoder.encoder.layer.1.attention.self.value.bias\n",
+ " encoder.encoder.layer.1.attention.output.dense.bias\n",
+ " encoder.encoder.layer.1.attention.output.LayerNorm.weight\n",
+ " encoder.encoder.layer.1.attention.output.LayerNorm.bias\n",
+ " encoder.encoder.layer.1.intermediate.dense.bias\n",
+ " encoder.encoder.layer.1.output.dense.bias\n",
+ " encoder.encoder.layer.1.output.LayerNorm.weight\n",
+ " encoder.encoder.layer.1.output.LayerNorm.bias\n",
+ " encoder.encoder.layer.2.attention.self.query.bias\n",
+ " encoder.encoder.layer.2.attention.self.key.bias\n",
+ " encoder.encoder.layer.2.attention.self.value.bias\n",
+ " encoder.encoder.layer.2.attention.output.dense.bias\n",
+ " encoder.encoder.layer.2.attention.output.LayerNorm.weight\n",
+ " encoder.encoder.layer.2.attention.output.LayerNorm.bias\n",
+ " encoder.encoder.layer.2.intermediate.dense.bias\n",
+ " encoder.encoder.layer.2.output.dense.bias\n",
+ " encoder.encoder.layer.2.output.LayerNorm.weight\n",
+ " encoder.encoder.layer.2.output.LayerNorm.bias\n",
+ " encoder.encoder.layer.3.attention.self.query.bias\n",
+ " encoder.encoder.layer.3.attention.self.key.bias\n",
+ " encoder.encoder.layer.3.attention.self.value.bias\n",
+ " encoder.encoder.layer.3.attention.output.dense.bias\n",
+ " encoder.encoder.layer.3.attention.output.LayerNorm.weight\n",
+ " encoder.encoder.layer.3.attention.output.LayerNorm.bias\n",
+ " encoder.encoder.layer.3.intermediate.dense.bias\n",
+ " encoder.encoder.layer.3.output.dense.bias\n",
+ " encoder.encoder.layer.3.output.LayerNorm.weight\n",
+ " encoder.encoder.layer.3.output.LayerNorm.bias\n",
+ " encoder.encoder.layer.4.attention.self.query.bias\n",
+ " encoder.encoder.layer.4.attention.self.key.bias\n",
+ " encoder.encoder.layer.4.attention.self.value.bias\n",
+ " encoder.encoder.layer.4.attention.output.dense.bias\n",
+ " encoder.encoder.layer.4.attention.output.LayerNorm.weight\n",
+ " encoder.encoder.layer.4.attention.output.LayerNorm.bias\n",
+ " encoder.encoder.layer.4.intermediate.dense.bias\n",
+ " encoder.encoder.layer.4.output.dense.bias\n",
+ " encoder.encoder.layer.4.output.LayerNorm.weight\n",
+ " encoder.encoder.layer.4.output.LayerNorm.bias\n",
+ " encoder.encoder.layer.5.attention.self.query.bias\n",
+ " encoder.encoder.layer.5.attention.self.key.bias\n",
+ " encoder.encoder.layer.5.attention.self.value.bias\n",
+ " encoder.encoder.layer.5.attention.output.dense.bias\n",
+ " encoder.encoder.layer.5.attention.output.LayerNorm.weight\n",
+ " encoder.encoder.layer.5.attention.output.LayerNorm.bias\n",
+ " encoder.encoder.layer.5.intermediate.dense.bias\n",
+ " encoder.encoder.layer.5.output.dense.bias\n",
+ " encoder.encoder.layer.5.output.LayerNorm.weight\n",
+ " encoder.encoder.layer.5.output.LayerNorm.bias\n",
+ " encoder.encoder.layer.6.attention.self.query.bias\n",
+ " encoder.encoder.layer.6.attention.self.key.bias\n",
+ " encoder.encoder.layer.6.attention.self.value.bias\n",
+ " encoder.encoder.layer.6.attention.output.dense.bias\n",
+ " encoder.encoder.layer.6.attention.output.LayerNorm.weight\n",
+ " encoder.encoder.layer.6.attention.output.LayerNorm.bias\n",
+ " encoder.encoder.layer.6.intermediate.dense.bias\n",
+ " encoder.encoder.layer.6.output.dense.bias\n",
+ " encoder.encoder.layer.6.output.LayerNorm.weight\n",
+ " encoder.encoder.layer.6.output.LayerNorm.bias\n",
+ " encoder.encoder.layer.7.attention.self.query.bias\n",
+ " encoder.encoder.layer.7.attention.self.key.bias\n",
+ " encoder.encoder.layer.7.attention.self.value.bias\n",
+ " encoder.encoder.layer.7.attention.output.dense.bias\n",
+ " encoder.encoder.layer.7.attention.output.LayerNorm.weight\n",
+ " encoder.encoder.layer.7.attention.output.LayerNorm.bias\n",
+ " encoder.encoder.layer.7.intermediate.dense.bias\n",
+ " encoder.encoder.layer.7.output.dense.bias\n",
+ " encoder.encoder.layer.7.output.LayerNorm.weight\n",
+ " encoder.encoder.layer.7.output.LayerNorm.bias\n",
+ " encoder.encoder.layer.8.attention.self.query.bias\n",
+ " encoder.encoder.layer.8.attention.self.key.bias\n",
+ " encoder.encoder.layer.8.attention.self.value.bias\n",
+ " encoder.encoder.layer.8.attention.output.dense.bias\n",
+ " encoder.encoder.layer.8.attention.output.LayerNorm.weight\n",
+ " encoder.encoder.layer.8.attention.output.LayerNorm.bias\n",
+ " encoder.encoder.layer.8.intermediate.dense.bias\n",
+ " encoder.encoder.layer.8.output.dense.bias\n",
+ " encoder.encoder.layer.8.output.LayerNorm.weight\n",
+ " encoder.encoder.layer.8.output.LayerNorm.bias\n",
+ " encoder.encoder.layer.9.attention.self.query.bias\n",
+ " encoder.encoder.layer.9.attention.self.key.bias\n",
+ " encoder.encoder.layer.9.attention.self.value.bias\n",
+ " encoder.encoder.layer.9.attention.output.dense.bias\n",
+ " encoder.encoder.layer.9.attention.output.LayerNorm.weight\n",
+ " encoder.encoder.layer.9.attention.output.LayerNorm.bias\n",
+ " encoder.encoder.layer.9.intermediate.dense.bias\n",
+ " encoder.encoder.layer.9.output.dense.bias\n",
+ " encoder.encoder.layer.9.output.LayerNorm.weight\n",
+ " encoder.encoder.layer.9.output.LayerNorm.bias\n",
+ " encoder.encoder.layer.10.attention.self.query.bias\n",
+ " encoder.encoder.layer.10.attention.self.key.bias\n",
+ " encoder.encoder.layer.10.attention.self.value.bias\n",
+ " encoder.encoder.layer.10.attention.output.dense.bias\n",
+ " encoder.encoder.layer.10.attention.output.LayerNorm.weight\n",
+ " encoder.encoder.layer.10.attention.output.LayerNorm.bias\n",
+ " encoder.encoder.layer.10.intermediate.dense.bias\n",
+ " encoder.encoder.layer.10.output.dense.bias\n",
+ " encoder.encoder.layer.10.output.LayerNorm.weight\n",
+ " encoder.encoder.layer.10.output.LayerNorm.bias\n",
+ " encoder.encoder.layer.11.attention.self.query.bias\n",
+ " encoder.encoder.layer.11.attention.self.key.bias\n",
+ " encoder.encoder.layer.11.attention.self.value.bias\n",
+ " encoder.encoder.layer.11.attention.output.dense.bias\n",
+ " encoder.encoder.layer.11.attention.output.LayerNorm.weight\n",
+ " encoder.encoder.layer.11.attention.output.LayerNorm.bias\n",
+ " encoder.encoder.layer.11.intermediate.dense.bias\n",
+ " encoder.encoder.layer.11.output.dense.bias\n",
+ " encoder.encoder.layer.11.output.LayerNorm.weight\n",
+ " encoder.encoder.layer.11.output.LayerNorm.bias\n",
+ " encoder.pooler.dense.bias\n",
+ " taskmodels_dict.semeval.span_comparison_head.span_attention_extractor._global_attention._module.bias\n",
+ " taskmodels_dict.semeval.span_comparison_head.classifier.bias\n",
+ "Using AdamW\n"
+ ],
+ "name": "stdout"
},
{
- "cell_type": "markdown",
- "metadata": {
- "id": "3MBuH19IWOr0"
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "f081100984b44e45a77fff620f998508",
+ "version_minor": 0,
+ "version_major": 2
},
- "source": [
- "#### Writing a run config\n",
- "\n",
- "Here we are going to write what we call a `jiant_task_container_config`. This configuration file basically defines a lot of the subtleties of our training pipeline, such as what tasks we will train on, do evaluation on, batch size for each task. The new version of `jiant` leans heavily toward explicitly specifying everything, for the purpose of inspectability and leaving minimal surprises for the user, even as the cost of being more verbose.\n",
- "\n",
- "We use a helper \"Configurator\" to write out a `jiant_task_container_config`, since most of our setup is pretty standard. \n",
- "\n",
- "**Depending on what GPU your Colab session is assigned to, you may need to lower the train batch size.**"
+ "text/plain": [
+ "HBox(children=(FloatProgress(value=0.0, description='Training', max=375.0, style=ProgressStyle(description_wid…"
]
+ },
+ "metadata": {
+ "tags": []
+ }
},
{
- "cell_type": "code",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "pQYtl7xTKsiP",
- "outputId": "00d58dc6-3d0d-40fa-8a3f-d19553803567"
- },
- "source": [
- "jiant_run_config = configurator.SimpleAPIMultiTaskConfigurator(\n",
- " task_config_base_path=\"./tasks/configs\",\n",
- " task_cache_base_path=\"./cache\",\n",
- " train_task_name_list=[\"semeval\"],\n",
- " val_task_name_list=[\"semeval\"],\n",
- " train_batch_size=8,\n",
- " eval_batch_size=16,\n",
- " epochs=3,\n",
- " num_gpus=1,\n",
- ").create_config()\n",
- "os.makedirs(\"./run_configs/\", exist_ok=True)\n",
- "py_io.write_json(jiant_run_config, \"./run_configs/semeval_run_config.json\")\n",
- "display.show_json(jiant_run_config)"
- ],
- "execution_count": 15,
- "outputs": [
- {
- "output_type": "stream",
- "text": [
- "{\n",
- " \"task_config_path_dict\": {\n",
- " \"semeval\": \"./tasks/configs/semeval_config.json\"\n",
- " },\n",
- " \"task_cache_config_dict\": {\n",
- " \"semeval\": {\n",
- " \"train\": \"./cache/semeval/train\",\n",
- " \"val\": \"./cache/semeval/val\",\n",
- " \"val_labels\": \"./cache/semeval/val_labels\"\n",
- " }\n",
- " },\n",
- " \"sampler_config\": {\n",
- " \"sampler_type\": \"ProportionalMultiTaskSampler\"\n",
- " },\n",
- " \"global_train_config\": {\n",
- " \"max_steps\": 375,\n",
- " \"warmup_steps\": 37\n",
- " },\n",
- " \"task_specific_configs_dict\": {\n",
- " \"semeval\": {\n",
- " \"train_batch_size\": 8,\n",
- " \"eval_batch_size\": 16,\n",
- " \"gradient_accumulation_steps\": 1,\n",
- " \"eval_subset_num\": 500\n",
- " }\n",
- " },\n",
- " \"taskmodels_config\": {\n",
- " \"task_to_taskmodel_map\": {\n",
- " \"semeval\": \"semeval\"\n",
- " },\n",
- " \"taskmodel_config_map\": {\n",
- " \"semeval\": null\n",
- " }\n",
- " },\n",
- " \"task_run_config\": {\n",
- " \"train_task_list\": [\n",
- " \"semeval\"\n",
- " ],\n",
- " \"train_val_task_list\": [\n",
- " \"semeval\"\n",
- " ],\n",
- " \"val_task_list\": [\n",
- " \"semeval\"\n",
- " ],\n",
- " \"test_task_list\": []\n",
- " },\n",
- " \"metric_aggregator_config\": {\n",
- " \"metric_aggregator_type\": \"EqualMetricAggregator\"\n",
- " }\n",
- "}\n"
- ],
- "name": "stdout"
- }
- ]
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ],
+ "name": "stdout"
},
{
- "cell_type": "markdown",
- "metadata": {
- "id": "-UF501yoXHBi"
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "7b6147ac6008406c932bb5c5fbf9c8ad",
+ "version_minor": 0,
+ "version_major": 2
},
- "source": [
- "To briefly go over the major components of the `jiant_task_container_config`:\n",
- "\n",
- "* `task_config_path_dict`: The paths to the task config files we wrote above.\n",
- "* `task_cache_config_dict`: The paths to the task features caches we generated above.\n",
- "* `sampler_config`: Determines how to sample from different tasks during training.\n",
- "* `global_train_config`: The number of total steps and warmup steps during training.\n",
- "* `task_specific_configs_dict`: Task-specific arguments for each task, such as training batch size and gradient accumulation steps.\n",
- "* `taskmodels_config`: Task-model specific arguments for each task-model, including what tasks use which model.\n",
- "* `metric_aggregator_config`: Determines how to weight/aggregate the metrics across multiple tasks."
+ "text/plain": [
+ "HBox(children=(FloatProgress(value=0.0, description='Eval (semeval, Val)', max=7.0, style=ProgressStyle(descri…"
]
+ },
+ "metadata": {
+ "tags": []
+ }
},
{
- "cell_type": "markdown",
- "metadata": {
- "id": "BBKkvXzdYPqZ"
- },
- "source": [
- "#### Start training\n",
- "\n",
- "Finally, we can start our training run. \n",
- "\n",
- "Before starting training, the script also prints out the list of parameters in our model."
- ]
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ],
+ "name": "stdout"
},
{
- "cell_type": "code",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 1000,
- "referenced_widgets": [
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- },
- "id": "JdwWPgjQWx6I",
- "outputId": "ba7e86d3-76e4-47bc-b61a-188783502323"
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "4b138e183eaf406a8fec6a2cc36f067a",
+ "version_minor": 0,
+ "version_major": 2
},
- "source": [
- "run_args = main_runscript.RunConfiguration(\n",
- " jiant_task_container_config_path=\"./run_configs/semeval_run_config.json\",\n",
- " output_dir=\"./runs/semeval\",\n",
- " hf_pretrained_model_name_or_path=\"roberta-base\",\n",
- " model_path=\"./models/roberta-base/model/roberta-base.p\",\n",
- " model_config_path=\"./models/roberta-base/model/roberta-base.json\",\n",
- " learning_rate=1e-5,\n",
- " eval_every_steps=500,\n",
- " do_train=True,\n",
- " do_val=True,\n",
- " do_save=True,\n",
- " force_overwrite=True,\n",
- ")\n",
- "main_runscript.run_loop(run_args)"
- ],
- "execution_count": 16,
- "outputs": [
- {
- "output_type": "stream",
- "text": [
- " jiant_task_container_config_path: ./run_configs/semeval_run_config.json\n",
- " output_dir: ./runs/semeval\n",
- " model_type: roberta-base\n",
- " model_path: ./models/roberta-base/model/roberta-base.p\n",
- " model_config_path: ./models/roberta-base/model/roberta-base.json\n",
- " model_load_mode: from_transformers\n",
- " do_train: True\n",
- " do_val: True\n",
- " do_save: True\n",
- " do_save_last: False\n",
- " do_save_best: False\n",
- " write_val_preds: False\n",
- " write_test_preds: False\n",
- " eval_every_steps: 500\n",
- " save_every_steps: 0\n",
- " save_checkpoint_every_steps: 0\n",
- " no_improvements_for_n_evals: 0\n",
- " keep_checkpoint_when_done: False\n",
- " force_overwrite: True\n",
- " seed: -1\n",
- " learning_rate: 1e-05\n",
- " adam_epsilon: 1e-08\n",
- " max_grad_norm: 1.0\n",
- " optimizer_type: adam\n",
- " no_cuda: False\n",
- " fp16: False\n",
- " fp16_opt_level: O1\n",
- " local_rank: -1\n",
- " server_ip: \n",
- " server_port: \n",
- "device: cuda n_gpu: 1, distributed training: False, 16-bits training: False\n",
- "Using seed: 195818355\n",
- "{\n",
- " \"jiant_task_container_config_path\": \"./run_configs/semeval_run_config.json\",\n",
- " \"output_dir\": \"./runs/semeval\",\n",
- " \"model_type\": \"roberta-base\",\n",
- " \"model_path\": \"./models/roberta-base/model/roberta-base.p\",\n",
- " \"model_config_path\": \"./models/roberta-base/model/roberta-base.json\",\n",
- " \"model_load_mode\": \"from_transformers\",\n",
- " \"do_train\": true,\n",
- " \"do_val\": true,\n",
- " \"do_save\": true,\n",
- " \"do_save_last\": false,\n",
- " \"do_save_best\": false,\n",
- " \"write_val_preds\": false,\n",
- " \"write_test_preds\": false,\n",
- " \"eval_every_steps\": 500,\n",
- " \"save_every_steps\": 0,\n",
- " \"save_checkpoint_every_steps\": 0,\n",
- " \"no_improvements_for_n_evals\": 0,\n",
- " \"keep_checkpoint_when_done\": false,\n",
- " \"force_overwrite\": true,\n",
- " \"seed\": 195818355,\n",
- " \"learning_rate\": 1e-05,\n",
- " \"adam_epsilon\": 1e-08,\n",
- " \"max_grad_norm\": 1.0,\n",
- " \"optimizer_type\": \"adam\",\n",
- " \"no_cuda\": false,\n",
- " \"fp16\": false,\n",
- " \"fp16_opt_level\": \"O1\",\n",
- " \"local_rank\": -1,\n",
- " \"server_ip\": \"\",\n",
- " \"server_port\": \"\"\n",
- "}\n",
- "1\n",
- "Creating Tasks:\n",
- " semeval (SemevalTask): ./tasks/configs/semeval_config.json\n"
- ],
- "name": "stdout"
- },
- {
- "output_type": "stream",
- "text": [
- "/content/jiant/jiant/proj/main/components/container_setup.py:78: UserWarning: task semeval from ./tasks/configs/semeval_config.json has conflicting names: semeval/semval. Using semeval\n",
- " task_name, task_config_path, task_name, task.name, task_name,\n"
- ],
- "name": "stderr"
- },
- {
- "output_type": "stream",
- "text": [
- "No optimizer decay for:\n",
- " encoder.embeddings.LayerNorm.weight\n",
- " encoder.embeddings.LayerNorm.bias\n",
- " encoder.encoder.layer.0.attention.self.query.bias\n",
- " encoder.encoder.layer.0.attention.self.key.bias\n",
- " encoder.encoder.layer.0.attention.self.value.bias\n",
- " encoder.encoder.layer.0.attention.output.dense.bias\n",
- " encoder.encoder.layer.0.attention.output.LayerNorm.weight\n",
- " encoder.encoder.layer.0.attention.output.LayerNorm.bias\n",
- " encoder.encoder.layer.0.intermediate.dense.bias\n",
- " encoder.encoder.layer.0.output.dense.bias\n",
- " encoder.encoder.layer.0.output.LayerNorm.weight\n",
- " encoder.encoder.layer.0.output.LayerNorm.bias\n",
- " encoder.encoder.layer.1.attention.self.query.bias\n",
- " encoder.encoder.layer.1.attention.self.key.bias\n",
- " encoder.encoder.layer.1.attention.self.value.bias\n",
- " encoder.encoder.layer.1.attention.output.dense.bias\n",
- " encoder.encoder.layer.1.attention.output.LayerNorm.weight\n",
- " encoder.encoder.layer.1.attention.output.LayerNorm.bias\n",
- " encoder.encoder.layer.1.intermediate.dense.bias\n",
- " encoder.encoder.layer.1.output.dense.bias\n",
- " encoder.encoder.layer.1.output.LayerNorm.weight\n",
- " encoder.encoder.layer.1.output.LayerNorm.bias\n",
- " encoder.encoder.layer.2.attention.self.query.bias\n",
- " encoder.encoder.layer.2.attention.self.key.bias\n",
- " encoder.encoder.layer.2.attention.self.value.bias\n",
- " encoder.encoder.layer.2.attention.output.dense.bias\n",
- " encoder.encoder.layer.2.attention.output.LayerNorm.weight\n",
- " encoder.encoder.layer.2.attention.output.LayerNorm.bias\n",
- " encoder.encoder.layer.2.intermediate.dense.bias\n",
- " encoder.encoder.layer.2.output.dense.bias\n",
- " encoder.encoder.layer.2.output.LayerNorm.weight\n",
- " encoder.encoder.layer.2.output.LayerNorm.bias\n",
- " encoder.encoder.layer.3.attention.self.query.bias\n",
- " encoder.encoder.layer.3.attention.self.key.bias\n",
- " encoder.encoder.layer.3.attention.self.value.bias\n",
- " encoder.encoder.layer.3.attention.output.dense.bias\n",
- " encoder.encoder.layer.3.attention.output.LayerNorm.weight\n",
- " encoder.encoder.layer.3.attention.output.LayerNorm.bias\n",
- " encoder.encoder.layer.3.intermediate.dense.bias\n",
- " encoder.encoder.layer.3.output.dense.bias\n",
- " encoder.encoder.layer.3.output.LayerNorm.weight\n",
- " encoder.encoder.layer.3.output.LayerNorm.bias\n",
- " encoder.encoder.layer.4.attention.self.query.bias\n",
- " encoder.encoder.layer.4.attention.self.key.bias\n",
- " encoder.encoder.layer.4.attention.self.value.bias\n",
- " encoder.encoder.layer.4.attention.output.dense.bias\n",
- " encoder.encoder.layer.4.attention.output.LayerNorm.weight\n",
- " encoder.encoder.layer.4.attention.output.LayerNorm.bias\n",
- " encoder.encoder.layer.4.intermediate.dense.bias\n",
- " encoder.encoder.layer.4.output.dense.bias\n",
- " encoder.encoder.layer.4.output.LayerNorm.weight\n",
- " encoder.encoder.layer.4.output.LayerNorm.bias\n",
- " encoder.encoder.layer.5.attention.self.query.bias\n",
- " encoder.encoder.layer.5.attention.self.key.bias\n",
- " encoder.encoder.layer.5.attention.self.value.bias\n",
- " encoder.encoder.layer.5.attention.output.dense.bias\n",
- " encoder.encoder.layer.5.attention.output.LayerNorm.weight\n",
- " encoder.encoder.layer.5.attention.output.LayerNorm.bias\n",
- " encoder.encoder.layer.5.intermediate.dense.bias\n",
- " encoder.encoder.layer.5.output.dense.bias\n",
- " encoder.encoder.layer.5.output.LayerNorm.weight\n",
- " encoder.encoder.layer.5.output.LayerNorm.bias\n",
- " encoder.encoder.layer.6.attention.self.query.bias\n",
- " encoder.encoder.layer.6.attention.self.key.bias\n",
- " encoder.encoder.layer.6.attention.self.value.bias\n",
- " encoder.encoder.layer.6.attention.output.dense.bias\n",
- " encoder.encoder.layer.6.attention.output.LayerNorm.weight\n",
- " encoder.encoder.layer.6.attention.output.LayerNorm.bias\n",
- " encoder.encoder.layer.6.intermediate.dense.bias\n",
- " encoder.encoder.layer.6.output.dense.bias\n",
- " encoder.encoder.layer.6.output.LayerNorm.weight\n",
- " encoder.encoder.layer.6.output.LayerNorm.bias\n",
- " encoder.encoder.layer.7.attention.self.query.bias\n",
- " encoder.encoder.layer.7.attention.self.key.bias\n",
- " encoder.encoder.layer.7.attention.self.value.bias\n",
- " encoder.encoder.layer.7.attention.output.dense.bias\n",
- " encoder.encoder.layer.7.attention.output.LayerNorm.weight\n",
- " encoder.encoder.layer.7.attention.output.LayerNorm.bias\n",
- " encoder.encoder.layer.7.intermediate.dense.bias\n",
- " encoder.encoder.layer.7.output.dense.bias\n",
- " encoder.encoder.layer.7.output.LayerNorm.weight\n",
- " encoder.encoder.layer.7.output.LayerNorm.bias\n",
- " encoder.encoder.layer.8.attention.self.query.bias\n",
- " encoder.encoder.layer.8.attention.self.key.bias\n",
- " encoder.encoder.layer.8.attention.self.value.bias\n",
- " encoder.encoder.layer.8.attention.output.dense.bias\n",
- " encoder.encoder.layer.8.attention.output.LayerNorm.weight\n",
- " encoder.encoder.layer.8.attention.output.LayerNorm.bias\n",
- " encoder.encoder.layer.8.intermediate.dense.bias\n",
- " encoder.encoder.layer.8.output.dense.bias\n",
- " encoder.encoder.layer.8.output.LayerNorm.weight\n",
- " encoder.encoder.layer.8.output.LayerNorm.bias\n",
- " encoder.encoder.layer.9.attention.self.query.bias\n",
- " encoder.encoder.layer.9.attention.self.key.bias\n",
- " encoder.encoder.layer.9.attention.self.value.bias\n",
- " encoder.encoder.layer.9.attention.output.dense.bias\n",
- " encoder.encoder.layer.9.attention.output.LayerNorm.weight\n",
- " encoder.encoder.layer.9.attention.output.LayerNorm.bias\n",
- " encoder.encoder.layer.9.intermediate.dense.bias\n",
- " encoder.encoder.layer.9.output.dense.bias\n",
- " encoder.encoder.layer.9.output.LayerNorm.weight\n",
- " encoder.encoder.layer.9.output.LayerNorm.bias\n",
- " encoder.encoder.layer.10.attention.self.query.bias\n",
- " encoder.encoder.layer.10.attention.self.key.bias\n",
- " encoder.encoder.layer.10.attention.self.value.bias\n",
- " encoder.encoder.layer.10.attention.output.dense.bias\n",
- " encoder.encoder.layer.10.attention.output.LayerNorm.weight\n",
- " encoder.encoder.layer.10.attention.output.LayerNorm.bias\n",
- " encoder.encoder.layer.10.intermediate.dense.bias\n",
- " encoder.encoder.layer.10.output.dense.bias\n",
- " encoder.encoder.layer.10.output.LayerNorm.weight\n",
- " encoder.encoder.layer.10.output.LayerNorm.bias\n",
- " encoder.encoder.layer.11.attention.self.query.bias\n",
- " encoder.encoder.layer.11.attention.self.key.bias\n",
- " encoder.encoder.layer.11.attention.self.value.bias\n",
- " encoder.encoder.layer.11.attention.output.dense.bias\n",
- " encoder.encoder.layer.11.attention.output.LayerNorm.weight\n",
- " encoder.encoder.layer.11.attention.output.LayerNorm.bias\n",
- " encoder.encoder.layer.11.intermediate.dense.bias\n",
- " encoder.encoder.layer.11.output.dense.bias\n",
- " encoder.encoder.layer.11.output.LayerNorm.weight\n",
- " encoder.encoder.layer.11.output.LayerNorm.bias\n",
- " encoder.pooler.dense.bias\n",
- " taskmodels_dict.semeval.span_comparison_head.span_attention_extractor._global_attention._module.bias\n",
- " taskmodels_dict.semeval.span_comparison_head.classifier.bias\n",
- "Using AdamW\n"
- ],
- "name": "stdout"
- },
- {
- "output_type": "display_data",
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "f081100984b44e45a77fff620f998508",
- "version_minor": 0,
- "version_major": 2
- },
- "text/plain": [
- "HBox(children=(FloatProgress(value=0.0, description='Training', max=375.0, style=ProgressStyle(description_wid…"
- ]
- },
- "metadata": {
- "tags": []
- }
- },
- {
- "output_type": "stream",
- "text": [
- "\n"
- ],
- "name": "stdout"
- },
- {
- "output_type": "display_data",
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "7b6147ac6008406c932bb5c5fbf9c8ad",
- "version_minor": 0,
- "version_major": 2
- },
- "text/plain": [
- "HBox(children=(FloatProgress(value=0.0, description='Eval (semeval, Val)', max=7.0, style=ProgressStyle(descri…"
- ]
- },
- "metadata": {
- "tags": []
- }
- },
- {
- "output_type": "stream",
- "text": [
- "\n"
- ],
- "name": "stdout"
- },
- {
- "output_type": "display_data",
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "4b138e183eaf406a8fec6a2cc36f067a",
- "version_minor": 0,
- "version_major": 2
- },
- "text/plain": [
- "HBox(children=(FloatProgress(value=0.0, description='Eval (semeval, Val)', max=7.0, style=ProgressStyle(descri…"
- ]
- },
- "metadata": {
- "tags": []
- }
- },
- {
- "output_type": "stream",
- "text": [
- "\n",
- "Loading Best\n"
- ],
- "name": "stdout"
- },
- {
- "output_type": "display_data",
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "ad747e2552c5429484dcd3d19c366a96",
- "version_minor": 0,
- "version_major": 2
- },
- "text/plain": [
- "HBox(children=(FloatProgress(value=0.0, description='Eval (semeval, Val)', max=7.0, style=ProgressStyle(descri…"
- ]
- },
- "metadata": {
- "tags": []
- }
- },
- {
- "output_type": "stream",
- "text": [
- "\n",
- "{\n",
- " \"aggregated\": 1.0,\n",
- " \"semeval\": {\n",
- " \"loss\": 0.007899788208305836,\n",
- " \"metrics\": {\n",
- " \"major\": 1.0,\n",
- " \"minor\": {\n",
- " \"acc\": 1.0,\n",
- " \"f1_micro\": 1.0,\n",
- " \"acc_and_f1_micro\": 1.0\n",
- " }\n",
- " }\n",
- " }\n",
- "}\n"
- ],
- "name": "stdout"
- }
+ "text/plain": [
+ "HBox(children=(FloatProgress(value=0.0, description='Eval (semeval, Val)', max=7.0, style=ProgressStyle(descri…"
]
+ },
+ "metadata": {
+ "tags": []
+ }
},
{
- "cell_type": "markdown",
- "metadata": {
- "id": "4SXcuHFIYp6Y"
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Loading Best\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "ad747e2552c5429484dcd3d19c366a96",
+ "version_minor": 0,
+ "version_major": 2
},
- "source": [
- "Since we're training and evaluating on the same (duplicated) example, we should get perfect performance, but hopefully this notebook should be illustrative of the workflow for edge-probing tasks."
+ "text/plain": [
+ "HBox(children=(FloatProgress(value=0.0, description='Eval (semeval, Val)', max=7.0, style=ProgressStyle(descri…"
]
+ },
+ "metadata": {
+ "tags": []
+ }
+ },
+ {
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "{\n",
+ " \"aggregated\": 1.0,\n",
+ " \"semeval\": {\n",
+ " \"loss\": 0.007899788208305836,\n",
+ " \"metrics\": {\n",
+ " \"major\": 1.0,\n",
+ " \"minor\": {\n",
+ " \"acc\": 1.0,\n",
+ " \"f1_micro\": 1.0,\n",
+ " \"acc_and_f1_micro\": 1.0\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n"
+ ],
+ "name": "stdout"
}
- ]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "4SXcuHFIYp6Y"
+ },
+ "source": [
+ "Since we're training and evaluating on the same (duplicated) example, we should get perfect performance, but hopefully this notebook should be illustrative of the workflow for edge-probing tasks."
+ ]
+ }
+ ]
}
\ No newline at end of file
diff --git a/examples/notebooks/jiant_MNLI_Diagnostic_Example.ipynb b/examples/notebooks/jiant_MNLI_Diagnostic_Example.ipynb
index a3a1eaff2..d2050752d 100644
--- a/examples/notebooks/jiant_MNLI_Diagnostic_Example.ipynb
+++ b/examples/notebooks/jiant_MNLI_Diagnostic_Example.ipynb
@@ -68,6 +68,7 @@
"outputs": [],
"source": [
"%%capture\n",
+ "%cd /content\n",
"# Download/preprocess MNLI and Dognostic data\n",
"!PYTHONPATH=/content/jiant python jiant/jiant/scripts/download_data/runscript.py \\\n",
" download \\\n",
@@ -321,8 +322,8 @@
" jiant_task_container_config_path=\"./run_configs/jiant_run_config.json\",\n",
" output_dir=\"./runs/run1\",\n",
" hf_pretrained_model_name_or_path=\"roberta-base\",\n",
- " model_path=\"./models/roberta-base/model/roberta-base.p\",\n",
- " model_config_path=\"./models/roberta-base/model/roberta-base.json\",\n",
+ " model_path=\"./models/roberta-base/model/roberta.p\",\n",
+ " model_config_path=\"./models/roberta-base/model/roberta.json\",\n",
" learning_rate=1e-5,\n",
" eval_every_steps=500,\n",
" do_train=True,\n",
@@ -376,4 +377,4 @@
},
"nbformat": 4,
"nbformat_minor": 1
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/jiant_Multi_Task_Example.ipynb b/examples/notebooks/jiant_Multi_Task_Example.ipynb
index 1ebbf465c..7f6c9acfa 100644
--- a/examples/notebooks/jiant_Multi_Task_Example.ipynb
+++ b/examples/notebooks/jiant_Multi_Task_Example.ipynb
@@ -88,6 +88,7 @@
"outputs": [],
"source": [
"%%capture\n",
+ "%cd /content\n",
"# Download RTE, STS-B and CommonsenseQA data\n",
"!PYTHONPATH=/content/jiant python jiant/jiant/scripts/download_data/runscript.py \\\n",
" download \\\n",
@@ -342,8 +343,8 @@
" jiant_task_container_config_path=\"./run_configs/jiant_run_config.json\",\n",
" output_dir=\"./runs/run1\",\n",
" hf_pretrained_model_name_or_path=\"roberta-base\",\n",
- " model_path=\"./models/roberta-base/model/roberta-base.p\",\n",
- " model_config_path=\"./models/roberta-base/model/roberta-base.json\",\n",
+ " model_path=\"./models/roberta-base/model/roberta.p\",\n",
+ " model_config_path=\"./models/roberta-base/model/roberta.json\",\n",
" learning_rate=1e-5,\n",
" eval_every_steps=500,\n",
" do_train=True,\n",
@@ -392,4 +393,4 @@
},
"nbformat": 4,
"nbformat_minor": 1
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/jiant_STILTs_Example.ipynb b/examples/notebooks/jiant_STILTs_Example.ipynb
index 2292f24f4..958eb883d 100644
--- a/examples/notebooks/jiant_STILTs_Example.ipynb
+++ b/examples/notebooks/jiant_STILTs_Example.ipynb
@@ -90,6 +90,7 @@
"outputs": [],
"source": [
"%%capture\n",
+ "%cd /content\n",
"# Download MNLI and RTE data\n",
"!PYTHONPATH=/content/jiant python jiant/jiant/scripts/download_data/runscript.py \\\n",
" download \\\n",
@@ -367,8 +368,8 @@
" jiant_task_container_config_path=\"./run_configs/mnli_run_config.json\",\n",
" output_dir=\"./runs/mnli\",\n",
" hf_pretrained_model_name_or_path=\"roberta-base\",\n",
- " model_path=\"./models/roberta-base/model/roberta-base.p\",\n",
- " model_config_path=\"./models/roberta-base/model/roberta-base.json\",\n",
+ " model_path=\"./models/roberta-base/model/roberta.p\",\n",
+ " model_config_path=\"./models/roberta-base/model/roberta.json\",\n",
" learning_rate=1e-5,\n",
" eval_every_steps=500,\n",
" do_train=True,\n",
@@ -405,7 +406,7 @@
" hf_pretrained_model_name_or_path=\"roberta-base\",\n",
" model_path=\"./runs/mnli/best_model.p\", # Loading the best model\n",
" model_load_mode=\"partial\",\n",
- " model_config_path=\"./models/roberta-base/model/roberta-base.json\",\n",
+ " model_config_path=\"./models/roberta-base/model/roberta.json\",\n",
" learning_rate=1e-5,\n",
" eval_every_steps=500,\n",
" do_train=True,\n",
@@ -454,4 +455,4 @@
},
"nbformat": 4,
"nbformat_minor": 1
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/jiant_XNLI_Example.ipynb b/examples/notebooks/jiant_XNLI_Example.ipynb
index 975f0f5a0..cdf0889b2 100644
--- a/examples/notebooks/jiant_XNLI_Example.ipynb
+++ b/examples/notebooks/jiant_XNLI_Example.ipynb
@@ -91,6 +91,7 @@
"outputs": [],
"source": [
"%%capture\n",
+ "%cd /content\n",
"# Download MNLI and XNLI data\n",
"!PYTHONPATH=/content/jiant python jiant/jiant/scripts/download_data/runscript.py \\\n",
" download \\\n",
@@ -383,8 +384,8 @@
" jiant_task_container_config_path=\"./run_configs/jiant_run_config.json\",\n",
" output_dir=\"./runs/run1\",\n",
" hf_pretrained_model_name_or_path=\"xlm-roberta-base\",\n",
- " model_path=\"./models/xlm-roberta-base/model/xlm-roberta-base.p\",\n",
- " model_config_path=\"./models/xlm-roberta-base/model/xlm-roberta-base.json\",\n",
+ " model_path=\"./models/xlm-roberta-base/model/xlm-roberta.p\",\n",
+ " model_config_path=\"./models/xlm-roberta-base/model/xlm-roberta.json\",\n",
" learning_rate=1e-5,\n",
" eval_every_steps=500,\n",
" do_train=True,\n",
@@ -433,4 +434,4 @@
},
"nbformat": 4,
"nbformat_minor": 1
-}
+}
\ No newline at end of file
diff --git a/guides/tasks/supported_tasks.md b/guides/tasks/supported_tasks.md
index 29e3413c8..c57842b42 100644
--- a/guides/tasks/supported_tasks.md
+++ b/guides/tasks/supported_tasks.md
@@ -7,8 +7,11 @@
| [Argument Reasoning Comprehension](https://arxiv.org/abs/1708.01425) | arct | ✅ | ✅ | arct | [Github](https://github.com/UKPLab/argument-reasoning-comprehension-task) |
| Abductive NLI | abductive_nli | ✅ | ✅ | abductive_nli | |
| SuperGLUE Winogender Diagnostic | superglue_axg | ✅ | ✅ | superglue_axg | SuperGLUE |
-| Acceptability Definiteness | acceptability_definiteness | ✅ | | acceptability_definiteness | Function Words |
-| Adversarial NLI | `adversarial_nli_{round}` | ✅ | | adversarial_nli | 3 rounds |
+| Acceptability Definiteness | acceptability_definiteness | ✅ | ✅ | acceptability_definiteness | Function Words |
+| Acceptability Coord | acceptability_coord | ✅ | ✅ | acceptability_coord | Function Words |
+| Acceptability EOS | acceptability_eos | ✅ | ✅ | acceptability_eos | Function Words |
+| Acceptability WH Words | acceptability_whwords | ✅ | ✅ | acceptability_whwords | Function Words |
+| Adversarial NLI | `adversarial_nli_{round}` | ✅ | ✅ | adversarial_nli | 3 rounds |
| ARC ("easy" version) | arc_easy | ✅ | ✅ | arc_easy | [site](https://allenai.org/data/arc) |
| ARC ("challenge" version) | arc_challenge | ✅ | ✅ | arc_challenge | [site](https://allenai.org/data/arc) |
| BoolQ | boolq | ✅ | ✅ | boolq | SuperGLUE |
@@ -55,7 +58,16 @@
| ReCord | record | ✅ | ✅ | record | SuperGLUE |
| RTE | rte | ✅ | ✅ | rte | GLUE, SuperGLUE |
| SciTail | scitail | ✅ | ✅ | scitail | |
-| SentEval: Tense | senteval_tense | ✅ | | senteval_tense | SentEval |
+| SentEval: Bigram Shift | senteval_bigram_shift | ✅ | ✅ | senteval_bigram_shift | SentEval |
+| SentEval: Coord Inversion | senteval_coordination_inversion | ✅ | ✅ | senteval_coordination_inversion | SentEval |
+| SentEval: Obj number | senteval_obj_number | ✅ | ✅ | senteval_obj_number | SentEval |
+| SentEval: Odd Man Out | senteval_odd_man_out | ✅ | ✅ | senteval_odd_man_out | SentEval |
+| SentEval: Past-Present | senteval_past_present | ✅ | ✅ | senteval_past_present | SentEval |
+| SentEval: Sentence Length | senteval_sentence_length | ✅ | ✅ | senteval_sentence_length | SentEval |
+| SentEval: Subj Number | senteval_subj_number | ✅ | ✅ | senteval_subj_number | SentEval |
+| SentEval: Top Constituents | senteval_top_constituents | ✅ | ✅ | senteval_top_constituents | SentEval |
+| SentEval: Tree Depth | senteval_tree_depth | ✅ | ✅ | senteval_tree_depth | SentEval |
+| SentEval: Word Content | senteval_word_content | ✅ | ✅ | senteval_word_content | SentEval |
| EP-Rel | semeval | ✅ | | semeval | Edge-Probing |
| SNLI | snli | ✅ | ✅ | snli | |
| SocialIQA | socialiqa | ✅ | ✅ | socialiqa | |
diff --git a/jiant/proj/main/export_model.py b/jiant/proj/main/export_model.py
index 7ceae03a8..dfd1cae32 100644
--- a/jiant/proj/main/export_model.py
+++ b/jiant/proj/main/export_model.py
@@ -23,7 +23,7 @@ def export_model(
- Tokenizer data
- JSON file pointing to paths for the above
Args:
- pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
+ hf_pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
Can be either:
- A string, the `model id` of a pretrained model configuration
diff --git a/jiant/proj/main/modeling/heads.py b/jiant/proj/main/modeling/heads.py
index b68c84b56..fca8a7e28 100644
--- a/jiant/proj/main/modeling/heads.py
+++ b/jiant/proj/main/modeling/heads.py
@@ -221,8 +221,10 @@ class BertMLMHead(BaseMLMHead):
def __init__(self, hidden_size, vocab_size, layer_norm_eps=1e-12, hidden_act="gelu"):
super().__init__()
self.dense = nn.Linear(hidden_size, hidden_size)
- self.transform_act_fn = transformers.modeling_bert.ACT2FN[hidden_act]
- self.LayerNorm = transformers.modeling_bert.BertLayerNorm(hidden_size, eps=layer_norm_eps)
+ self.transform_act_fn = transformers.models.bert.modeling_bert.ACT2FN[hidden_act]
+ self.LayerNorm = transformers.models.bert.modeling_bert.BertLayerNorm(
+ hidden_size, eps=layer_norm_eps
+ )
self.decoder = nn.Linear(hidden_size, vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros(vocab_size), requires_grad=True)
@@ -246,7 +248,9 @@ class RobertaMLMHead(BaseMLMHead):
def __init__(self, hidden_size, vocab_size, layer_norm_eps=1e-12):
super().__init__()
self.dense = nn.Linear(hidden_size, hidden_size)
- self.layer_norm = transformers.modeling_bert.BertLayerNorm(hidden_size, eps=layer_norm_eps)
+ self.layer_norm = transformers.models.bert.modeling_bert.BertLayerNorm(
+ hidden_size, eps=layer_norm_eps
+ )
self.decoder = nn.Linear(hidden_size, vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros(vocab_size), requires_grad=True)
@@ -257,7 +261,7 @@ def __init__(self, hidden_size, vocab_size, layer_norm_eps=1e-12):
def forward(self, unpooled):
x = self.dense(unpooled)
- x = transformers.modeling_bert.gelu(x)
+ x = transformers.models.bert.modeling_bert.gelu(x)
x = self.layer_norm(x)
# project back to size of vocabulary with bias
@@ -276,7 +280,7 @@ def __init__(self, hidden_size, embedding_size, vocab_size, hidden_act="gelu"):
self.bias = nn.Parameter(torch.zeros(vocab_size), requires_grad=True)
self.dense = nn.Linear(hidden_size, embedding_size)
self.decoder = nn.Linear(embedding_size, vocab_size)
- self.activation = transformers.modeling_bert.ACT2FN[hidden_act]
+ self.activation = transformers.models.bert.modeling_bert.ACT2FN[hidden_act]
# Need a link between the two variables so that the bias is correctly resized with
# `resize_token_embeddings`
diff --git a/jiant/proj/main/modeling/taskmodels.py b/jiant/proj/main/modeling/taskmodels.py
index 65df810e8..583570fe4 100644
--- a/jiant/proj/main/modeling/taskmodels.py
+++ b/jiant/proj/main/modeling/taskmodels.py
@@ -8,10 +8,8 @@
import torch.nn as nn
import jiant.proj.main.modeling.heads as heads
-import jiant.utils.transformer_utils as transformer_utils
-
-from jiant.proj.main.components.outputs import LogitsAndLossOutput
-from jiant.proj.main.components.outputs import LogitsOutput
+from jiant.proj.main.components.outputs import LogitsOutput, LogitsAndLossOutput
+from jiant.utils.python.datastructures import take_one
from jiant.shared.model_resolution import ModelArchitectures
from jiant.tasks.core import TaskTypes
from jiant.utils.python.datastructures import take_one
@@ -284,9 +282,11 @@ def __init__(self, task, encoder, head: heads.AbstractPoolerHead, **kwargs):
super().__init__(task=task, encoder=encoder, head=head)
self.layer = kwargs["layer"]
- def forward(self, batch, tokenizer, compute_loss: bool = False):
- with transformer_utils.output_hidden_states_context(self.encoder):
- encoder_output = get_output_from_encoder_and_batch(encoder=self.encoder, batch=batch)
+ def forward(self, batch, task, tokenizer, compute_loss: bool = False):
+ encoder_output = get_output_from_encoder_and_batch(
+ encoder_output=self.encoder, batch=batch, output_hidden_states=True
+ )
+
# A tuple of layers of hidden states
hidden_states = take_one(encoder_output.other)
layer_hidden_states = hidden_states[self.layer]
@@ -319,7 +319,7 @@ class EncoderOutput:
# Extend later with attention, hidden_acts, etc
-def get_output_from_encoder_and_batch(encoder, batch) -> EncoderOutput:
+def get_output_from_encoder_and_batch(encoder, batch, output_hidden_states=False) -> EncoderOutput:
"""Pass batch to encoder, return encoder model output.
Args:
@@ -335,10 +335,13 @@ def get_output_from_encoder_and_batch(encoder, batch) -> EncoderOutput:
input_ids=batch.input_ids,
segment_ids=batch.segment_ids,
input_mask=batch.input_mask,
+ output_hidden_states=output_hidden_states,
)
-def get_output_from_encoder(encoder, input_ids, segment_ids, input_mask) -> EncoderOutput:
+def get_output_from_encoder(
+ encoder, input_ids, segment_ids, input_mask, output_hidden_states=False
+) -> EncoderOutput:
"""Pass inputs to encoder, return encoder output.
Args:
@@ -362,18 +365,29 @@ def get_output_from_encoder(encoder, input_ids, segment_ids, input_mask) -> Enco
ModelArchitectures.XLM_ROBERTA,
]:
pooled, unpooled, other = get_output_from_standard_transformer_models(
- encoder=encoder, input_ids=input_ids, segment_ids=segment_ids, input_mask=input_mask,
+ encoder=encoder,
+ input_ids=input_ids,
+ segment_ids=segment_ids,
+ input_mask=input_mask,
+ output_hidden_states=output_hidden_states,
)
elif model_arch == ModelArchitectures.ELECTRA:
pooled, unpooled, other = get_output_from_electra(
- encoder=encoder, input_ids=input_ids, segment_ids=segment_ids, input_mask=input_mask,
+ encoder=encoder,
+ input_ids=input_ids,
+ segment_ids=segment_ids,
+ input_mask=input_mask,
+ output_hidden_states=output_hidden_states,
)
elif model_arch in [
ModelArchitectures.BART,
ModelArchitectures.MBART,
]:
pooled, unpooled, other = get_output_from_bart_models(
- encoder=encoder, input_ids=input_ids, input_mask=input_mask,
+ encoder=encoder,
+ input_ids=input_ids,
+ input_mask=input_mask,
+ output_hidden_states=output_hidden_states,
)
else:
raise KeyError(model_arch)
@@ -385,38 +399,54 @@ def get_output_from_encoder(encoder, input_ids, segment_ids, input_mask) -> Enco
return EncoderOutput(pooled=pooled, unpooled=unpooled)
-def get_output_from_standard_transformer_models(encoder, input_ids, segment_ids, input_mask):
- output = encoder(input_ids=input_ids, token_type_ids=segment_ids, attention_mask=input_mask)
- pooled, unpooled, other = output[1], output[0], output[2:]
- return pooled, unpooled, other
+def get_output_from_standard_transformer_models(
+ encoder, input_ids, segment_ids, input_mask, output_hidden_states=False
+):
+ output = encoder(
+ input_ids=input_ids,
+ token_type_ids=segment_ids,
+ attention_mask=input_mask,
+ output_hidden_states=output_hidden_states,
+ )
+ return output.pooler_output, output.last_hidden_state, output.hidden_states
-def get_output_from_bart_models(encoder, input_ids, input_mask):
+def get_output_from_bart_models(encoder, input_ids, input_mask, output_hidden_states=False):
# BART and mBART and encoder-decoder architectures.
# As described in the BART paper and implemented in Transformers,
# for single input tasks, the encoder input is the sequence,
# the decode input is 1-shifted sequence, and the resulting
# sentence representation is the final decoder state.
# That's what we use for `unpooled` here.
- dec_last, dec_all, enc_last, enc_all = encoder(
- input_ids=input_ids, attention_mask=input_mask, output_hidden_states=True,
+ output = encoder(
+ input_ids=input_ids, attention_mask=input_mask, output_hidden_states=output_hidden_states,
)
- unpooled = dec_last
+ dec_all = output.decoder_hidden_states
+ enc_all = output.encoder_hidden_states
- other = (enc_all + dec_all,)
+ unpooled = output
+
+ hidden_states = (enc_all + dec_all,)
bsize, slen = input_ids.shape
batch_idx = torch.arange(bsize).to(input_ids.device)
# Get last non-pad index
pooled = unpooled[batch_idx, slen - input_ids.eq(encoder.config.pad_token_id).sum(1) - 1]
- return pooled, unpooled, other
+ return pooled, unpooled, hidden_states
-def get_output_from_electra(encoder, input_ids, segment_ids, input_mask):
- output = encoder(input_ids=input_ids, token_type_ids=segment_ids, attention_mask=input_mask)
- unpooled = output[0]
+def get_output_from_electra(
+ encoder, input_ids, segment_ids, input_mask, output_hidden_states=False
+):
+ output = encoder(
+ input_ids=input_ids,
+ token_type_ids=segment_ids,
+ attention_mask=input_mask,
+ output_hidden_states=output_hidden_states,
+ )
+ unpooled = output.hidden_states
pooled = unpooled[:, 0, :]
- return pooled, unpooled, output
+ return pooled, unpooled, output.hidden_states
def compute_mlm_loss(logits, masked_lm_labels):
diff --git a/jiant/proj/main/runscript.py b/jiant/proj/main/runscript.py
index 11ee44ad9..ba6053323 100644
--- a/jiant/proj/main/runscript.py
+++ b/jiant/proj/main/runscript.py
@@ -22,7 +22,6 @@ class RunConfiguration(zconf.RunConfig):
# === Model parameters === #
hf_pretrained_model_name_or_path = zconf.attr(type=str, required=True)
- model_type = zconf.attr(type=str, required=True)
model_path = zconf.attr(type=str, required=True)
model_config_path = zconf.attr(default=None, type=str)
model_load_mode = zconf.attr(default="from_transformers", type=str)
diff --git a/jiant/scripts/download_data/constants.py b/jiant/scripts/download_data/constants.py
index badd231c4..132a6c489 100644
--- a/jiant/scripts/download_data/constants.py
+++ b/jiant/scripts/download_data/constants.py
@@ -24,6 +24,20 @@
"piqa",
"winogrande",
"ropes",
+ "acceptability_definiteness",
+ "acceptability_coord",
+ "acceptability_eos",
+ "acceptability_whwords",
+ "senteval_bigram_shift",
+ "senteval_coordination_inversion",
+ "senteval_obj_number",
+ "senteval_odd_man_out",
+ "senteval_past_present",
+ "senteval_sentence_length",
+ "senteval_subj_number",
+ "senteval_top_constituents",
+ "senteval_tree_depth",
+ "senteval_word_content",
}
DIRECT_DOWNLOAD_TASKS = set(
diff --git a/jiant/scripts/download_data/dl_datasets/files_tasks.py b/jiant/scripts/download_data/dl_datasets/files_tasks.py
index b943b8fb6..1ff772aba 100644
--- a/jiant/scripts/download_data/dl_datasets/files_tasks.py
+++ b/jiant/scripts/download_data/dl_datasets/files_tasks.py
@@ -89,6 +89,30 @@ def download_task_data_and_write_config(task_name: str, task_data_path: str, tas
download_ropes_data_and_write_config(
task_name=task_name, task_data_path=task_data_path, task_config_path=task_config_path
)
+ elif task_name in [
+ "acceptability_definiteness",
+ "acceptability_coord",
+ "acceptability_eos",
+ "acceptability_whwords",
+ ]:
+ download_acceptability_judgments_data_and_write_config(
+ task_name=task_name, task_data_path=task_data_path, task_config_path=task_config_path
+ )
+ elif task_name in [
+ "senteval_bigram_shift",
+ "senteval_coordination_inversion",
+ "senteval_obj_number",
+ "senteval_odd_man_out",
+ "senteval_past_present",
+ "senteval_sentence_length",
+ "senteval_subj_number",
+ "senteval_top_constituents",
+ "senteval_tree_depth",
+ "senteval_word_content",
+ ]:
+ download_senteval_data_and_write_config(
+ task_name=task_name, task_data_path=task_data_path, task_config_path=task_config_path
+ )
else:
raise KeyError(task_name)
@@ -1012,3 +1036,69 @@ def download_ropes_data_and_write_config(
},
path=task_config_path,
)
+
+
+def download_acceptability_judgments_data_and_write_config(
+ task_name: str, task_data_path: str, task_config_path: str
+):
+ dataset_name = {
+ "acceptability_definiteness": "definiteness",
+ "acceptability_coord": "coordinating-conjunctions",
+ "acceptability_whwords": "whwords",
+ "acceptability_eos": "eos",
+ }[task_name]
+ os.makedirs(task_data_path, exist_ok=True)
+ # data contains all train/val/test examples
+ # metadata contains the split indicators
+ # (there are 10 CV folds, we use fold1 by default, see below)
+ data_path = os.path.join(task_data_path, "data.json")
+ metadata_path = os.path.join(task_data_path, "metadata.json")
+ download_utils.download_file(
+ url="https://raw.githubusercontent.com/decompositional-semantics-initiative/DNC/master/"
+ f"function_words/ACCEPTABILITY/acceptability-{dataset_name}_data.json",
+ file_path=data_path,
+ )
+ download_utils.download_file(
+ url="https://raw.githubusercontent.com/decompositional-semantics-initiative/DNC/master/"
+ f"function_words/ACCEPTABILITY/acceptability-{dataset_name}_metadata.json",
+ file_path=metadata_path,
+ )
+ py_io.write_json(
+ data={
+ "task": task_name,
+ "paths": {"data": data_path, "metadata": metadata_path},
+ "name": task_name,
+ "kwargs": {"fold": "fold1"}, # use fold1 (out of 10) by default
+ },
+ path=task_config_path,
+ )
+
+
+def download_senteval_data_and_write_config(
+ task_name: str, task_data_path: str, task_config_path: str
+):
+ name_map = {
+ "senteval_bigram_shift": "bigram_shift",
+ "senteval_coordination_inversion": "coordination_inversion",
+ "senteval_obj_number": "obj_number",
+ "senteval_odd_man_out": "odd_man_out",
+ "senteval_past_present": "past_present",
+ "senteval_sentence_length": "sentence_length",
+ "senteval_subj_number": "subj_number",
+ "senteval_top_constituents": "top_constituents",
+ "senteval_tree_depth": "tree_depth",
+ "senteval_word_content": "word_content",
+ }
+ dataset_name = name_map[task_name]
+ os.makedirs(task_data_path, exist_ok=True)
+ # data contains all train/val/test examples, first column indicates the split
+ data_path = os.path.join(task_data_path, "data.tsv")
+ download_utils.download_file(
+ url="https://raw.githubusercontent.com/facebookresearch/SentEval/master/data/probing/"
+ f"{dataset_name}.txt",
+ file_path=data_path,
+ )
+ py_io.write_json(
+ data={"task": task_name, "paths": {"data": data_path}, "name": task_name},
+ path=task_config_path,
+ )
diff --git a/jiant/scripts/download_data/runscript.py b/jiant/scripts/download_data/runscript.py
index 65d2f90c9..566e3a593 100644
--- a/jiant/scripts/download_data/runscript.py
+++ b/jiant/scripts/download_data/runscript.py
@@ -47,7 +47,9 @@ def download_data(task_names, output_base_path):
task_data_base_path = py_io.create_dir(output_base_path, "data")
task_config_base_path = py_io.create_dir(output_base_path, "configs")
- assert set(task_names).issubset(SUPPORTED_TASKS)
+ assert set(task_names).issubset(SUPPORTED_TASKS), "Following tasks are not support: {}".format(
+ ",".join(set(task_names) - SUPPORTED_TASKS)
+ )
# Download specified tasks and generate configs for specified tasks
for i, task_name in enumerate(task_names):
diff --git a/jiant/tasks/evaluate/core.py b/jiant/tasks/evaluate/core.py
index 8b3d3638a..fbd535692 100644
--- a/jiant/tasks/evaluate/core.py
+++ b/jiant/tasks/evaluate/core.py
@@ -957,6 +957,9 @@ def get_evaluation_scheme_for_task(task) -> BaseEvaluationScheme:
tasks.AdversarialNliTask,
tasks.AbductiveNliTask,
tasks.AcceptabilityDefinitenessTask,
+ tasks.AcceptabilityCoordTask,
+ tasks.AcceptabilityEOSTask,
+ tasks.AcceptabilityWHwordsTask,
tasks.BoolQTask,
tasks.CopaTask,
tasks.FeverNliTask,
@@ -966,7 +969,16 @@ def get_evaluation_scheme_for_task(task) -> BaseEvaluationScheme:
tasks.RaceTask,
tasks.RteTask,
tasks.SciTailTask,
- tasks.SentevalTenseTask,
+ tasks.SentEvalBigramShiftTask,
+ tasks.SentEvalCoordinationInversionTask,
+ tasks.SentEvalObjNumberTask,
+ tasks.SentEvalOddManOutTask,
+ tasks.SentEvalPastPresentTask,
+ tasks.SentEvalSentenceLengthTask,
+ tasks.SentEvalSubjNumberTask,
+ tasks.SentEvalTopConstituentsTask,
+ tasks.SentEvalTreeDepthTask,
+ tasks.SentEvalWordContentTask,
tasks.SnliTask,
tasks.SstTask,
tasks.WiCTask,
diff --git a/jiant/tasks/lib/acceptability_judgement/base.py b/jiant/tasks/lib/acceptability_judgement/base.py
new file mode 100644
index 000000000..5f05e83bf
--- /dev/null
+++ b/jiant/tasks/lib/acceptability_judgement/base.py
@@ -0,0 +1,107 @@
+import numpy as np
+import torch
+from dataclasses import dataclass
+from typing import List
+
+from jiant.tasks.core import (
+ BaseExample,
+ BaseTokenizedExample,
+ BaseDataRow,
+ BatchMixin,
+ Task,
+ TaskTypes,
+)
+from jiant.tasks.lib.templates.shared import single_sentence_featurize, labels_to_bimap
+from jiant.utils.python.io import read_json
+
+
+@dataclass
+class Example(BaseExample):
+ guid: str
+ text: str
+ label: str
+
+ def tokenize(self, tokenizer):
+ return TokenizedExample(
+ guid=self.guid,
+ text=tokenizer.tokenize(self.text),
+ label_id=BaseAcceptabilityTask.LABEL_TO_ID[self.label],
+ )
+
+
+@dataclass
+class TokenizedExample(BaseTokenizedExample):
+ guid: str
+ text: List
+ label_id: int
+
+ def featurize(self, tokenizer, feat_spec):
+ return single_sentence_featurize(
+ guid=self.guid,
+ input_tokens=self.text,
+ label_id=self.label_id,
+ tokenizer=tokenizer,
+ feat_spec=feat_spec,
+ data_row_class=DataRow,
+ )
+
+
+@dataclass
+class DataRow(BaseDataRow):
+ guid: str
+ input_ids: np.ndarray
+ input_mask: np.ndarray
+ segment_ids: np.ndarray
+ label_id: int
+ tokens: list
+
+
+@dataclass
+class Batch(BatchMixin):
+ input_ids: torch.LongTensor
+ input_mask: torch.LongTensor
+ segment_ids: torch.LongTensor
+ label_id: torch.LongTensor
+ tokens: list
+
+
+class BaseAcceptabilityTask(Task):
+ Example = Example
+ TokenizedExample = TokenizedExample
+ DataRow = DataRow
+ Batch = Batch
+
+ TASK_TYPE = TaskTypes.CLASSIFICATION
+ LABELS = ["acceptable", "unacceptable"]
+ LABEL_TO_ID, ID_TO_LABEL = labels_to_bimap(LABELS)
+ DATA_PHASE_MAP = {"train": "train", "dev": "val", "test": "test"}
+
+ def __init__(self, name, path_dict, fold: str):
+ # Fold should be a string like "fold1"
+ super().__init__(name=name, path_dict=path_dict)
+ self.fold = fold
+
+ def get_train_examples(self):
+ return self._create_examples(set_type="train")
+
+ def get_val_examples(self):
+ return self._create_examples(set_type="val")
+
+ def get_test_examples(self):
+ return self._create_examples(set_type="test")
+
+ def _create_examples(self, set_type):
+ data = read_json(self.path_dict["data"])
+ metadata = read_json(self.path_dict["metadata"])
+ assert len(data) == len(metadata)
+ examples = []
+ for data_row, metadata_row in zip(data, metadata):
+ row_phase = self.DATA_PHASE_MAP[metadata_row["misc"][self.fold]]
+ if row_phase != set_type:
+ continue
+ examples.append(
+ Example(
+ guid=data_row["pair-id"], text=data_row["context"], label=data_row["label"],
+ )
+ )
+ return examples
diff --git a/jiant/tasks/lib/acceptability_judgement/coord.py b/jiant/tasks/lib/acceptability_judgement/coord.py
new file mode 100644
index 000000000..3f4e29b57
--- /dev/null
+++ b/jiant/tasks/lib/acceptability_judgement/coord.py
@@ -0,0 +1,26 @@
+from dataclasses import dataclass
+import jiant.tasks.lib.acceptability_judgement.base as base
+
+
+@dataclass
+class Example(base.Example):
+ pass
+
+
+@dataclass
+class TokenizedExample(base.TokenizedExample):
+ pass
+
+
+@dataclass
+class DataRow(base.DataRow):
+ pass
+
+
+@dataclass
+class Batch(base.Batch):
+ pass
+
+
+class AcceptabilityCoordTask(base.BaseAcceptabilityTask):
+ pass
diff --git a/jiant/tasks/lib/acceptability_judgement/definiteness.py b/jiant/tasks/lib/acceptability_judgement/definiteness.py
index 0232d817c..499ee2b2c 100644
--- a/jiant/tasks/lib/acceptability_judgement/definiteness.py
+++ b/jiant/tasks/lib/acceptability_judgement/definiteness.py
@@ -1,99 +1,26 @@
-import numpy as np
-import pandas as pd
-import torch
from dataclasses import dataclass
-from typing import List
-
-from jiant.tasks.core import (
- BaseExample,
- BaseTokenizedExample,
- BaseDataRow,
- BatchMixin,
- Task,
- TaskTypes,
-)
-from jiant.tasks.lib.templates.shared import single_sentence_featurize, labels_to_bimap
+import jiant.tasks.lib.acceptability_judgement.base as base
@dataclass
-class Example(BaseExample):
- guid: str
- text: str
- label: str
-
- def tokenize(self, tokenizer):
- return TokenizedExample(
- guid=self.guid,
- text=tokenizer.tokenize(self.text),
- label_id=AcceptabilityDefinitenessTask.LABEL_TO_ID[self.label],
- )
+class Example(base.Example):
+ pass
@dataclass
-class TokenizedExample(BaseTokenizedExample):
- guid: str
- text: List
- label_id: int
-
- def featurize(self, tokenizer, feat_spec):
- return single_sentence_featurize(
- guid=self.guid,
- input_tokens=self.text,
- label_id=self.label_id,
- tokenizer=tokenizer,
- feat_spec=feat_spec,
- data_row_class=DataRow,
- )
+class TokenizedExample(base.TokenizedExample):
+ pass
@dataclass
-class DataRow(BaseDataRow):
- guid: str
- input_ids: np.ndarray
- input_mask: np.ndarray
- segment_ids: np.ndarray
- label_id: int
- tokens: list
+class DataRow(base.DataRow):
+ pass
@dataclass
-class Batch(BatchMixin):
- input_ids: torch.LongTensor
- input_mask: torch.LongTensor
- segment_ids: torch.LongTensor
- label_id: torch.LongTensor
- tokens: list
-
-
-class AcceptabilityDefinitenessTask(Task):
- Example = Example
- TokenizedExample = TokenizedExample
- DataRow = DataRow
- Batch = Batch
-
- TASK_TYPE = TaskTypes.CLASSIFICATION
- LABELS = ["acceptable", "unacceptable"]
- LABEL_TO_ID, ID_TO_LABEL = labels_to_bimap(LABELS)
-
- def get_train_examples(self):
- return self._create_examples(path=self.train_path, set_type="train")
-
- def get_val_examples(self):
- return self._create_examples(path=self.val_path, set_type="val")
+class Batch(base.Batch):
+ pass
- def get_test_examples(self):
- return self._create_examples(path=self.test_path, set_type="test")
- @classmethod
- def _create_examples(cls, path, set_type):
- examples = []
- df = pd.read_csv(path, sep="\t", index_col=0, names=["text", "label"])
- for i, row in df.iterrows():
- examples.append(
- Example(
- guid="%s-%s" % (set_type, i),
- text=row.text,
- label=row.label if set_type != "test" else cls.LABELS[-1],
- )
- )
- return examples
+class AcceptabilityDefinitenessTask(base.BaseAcceptabilityTask):
+ pass
diff --git a/jiant/tasks/lib/acceptability_judgement/eos.py b/jiant/tasks/lib/acceptability_judgement/eos.py
new file mode 100644
index 000000000..2fd83daf6
--- /dev/null
+++ b/jiant/tasks/lib/acceptability_judgement/eos.py
@@ -0,0 +1,26 @@
+from dataclasses import dataclass
+import jiant.tasks.lib.acceptability_judgement.base as base
+
+
+@dataclass
+class Example(base.Example):
+ pass
+
+
+@dataclass
+class TokenizedExample(base.TokenizedExample):
+ pass
+
+
+@dataclass
+class DataRow(base.DataRow):
+ pass
+
+
+@dataclass
+class Batch(base.Batch):
+ pass
+
+
+class AcceptabilityEOSTask(base.BaseAcceptabilityTask):
+ pass
diff --git a/jiant/tasks/lib/acceptability_judgement/whwords.py b/jiant/tasks/lib/acceptability_judgement/whwords.py
new file mode 100644
index 000000000..c1be84118
--- /dev/null
+++ b/jiant/tasks/lib/acceptability_judgement/whwords.py
@@ -0,0 +1,26 @@
+from dataclasses import dataclass
+import jiant.tasks.lib.acceptability_judgement.base as base
+
+
+@dataclass
+class Example(base.Example):
+ pass
+
+
+@dataclass
+class TokenizedExample(base.TokenizedExample):
+ pass
+
+
+@dataclass
+class DataRow(base.DataRow):
+ pass
+
+
+@dataclass
+class Batch(base.Batch):
+ pass
+
+
+class AcceptabilityWHwordsTask(base.BaseAcceptabilityTask):
+ pass
diff --git a/jiant/tasks/lib/ropes.py b/jiant/tasks/lib/ropes.py
index 74c823ad7..8c022bf0e 100644
--- a/jiant/tasks/lib/ropes.py
+++ b/jiant/tasks/lib/ropes.py
@@ -90,7 +90,9 @@ def to_feature_list(
# (This may not apply for future added models that don't start with a CLS token,
# such as XLNet/GPT-2)
sequence_added_tokens = 1
- sequence_pair_added_tokens = tokenizer.max_len - tokenizer.max_len_sentences_pair
+ sequence_pair_added_tokens = (
+ tokenizer.model_max_length - tokenizer.model_max_length_sentences_pair
+ )
span_doc_tokens = all_doc_tokens
while len(spans) * doc_stride < len(all_doc_tokens):
diff --git a/jiant/tasks/lib/senteval/tense.py b/jiant/tasks/lib/senteval/base.py
similarity index 68%
rename from jiant/tasks/lib/senteval/tense.py
rename to jiant/tasks/lib/senteval/base.py
index 96d77cc79..54c5c4067 100644
--- a/jiant/tasks/lib/senteval/tense.py
+++ b/jiant/tasks/lib/senteval/base.py
@@ -12,7 +12,7 @@
Task,
TaskTypes,
)
-from jiant.tasks.lib.templates.shared import single_sentence_featurize, labels_to_bimap
+from jiant.tasks.lib.templates.shared import single_sentence_featurize
@dataclass
@@ -21,11 +21,15 @@ class Example(BaseExample):
text: str
label: str
+ @property
+ def label_to_id(self):
+ raise NotImplementedError()
+
def tokenize(self, tokenizer):
return TokenizedExample(
guid=self.guid,
text=tokenizer.tokenize(self.text),
- label_id=SentevalTenseTask.LABEL_TO_ID[self.label],
+ label_id=self.label_to_id[self.label],
)
@@ -65,35 +69,36 @@ class Batch(BatchMixin):
tokens: list
-class SentevalTenseTask(Task):
+class BaseSentEvalTask(Task):
Example = Example
TokenizedExample = TokenizedExample
DataRow = DataRow
Batch = Batch
TASK_TYPE = TaskTypes.CLASSIFICATION
- LABELS = ["PAST", "PRES"]
- LABEL_TO_ID, ID_TO_LABEL = labels_to_bimap(LABELS)
+ LABELS = None # Override this
def get_train_examples(self):
- return self._create_examples(path=self.train_path, set_type="train")
+ return self._create_examples(set_type="train")
def get_val_examples(self):
- return self._create_examples(path=self.val_path, set_type="val")
+ return self._create_examples(set_type="val")
def get_test_examples(self):
- return self._create_examples(path=self.test_path, set_type="test")
+ return self._create_examples(set_type="test")
- @classmethod
- def _create_examples(cls, path, set_type):
+ def _create_examples(self, set_type):
examples = []
- df = pd.read_csv(path, index_col=0, names=["split", "label", "text", "unk_1", "unk_2"])
- for i, row in df.iterrows():
+ df = pd.read_csv(self.path_dict["data"], sep="\t", names=["phase", "label", "text"])
+ phase_key = {"train": "tr", "val": "va", "test": "te"}[set_type]
+ sub_df = df[df["phase"] == phase_key]
+ for i, row in sub_df.iterrows():
+ # noinspection PyArgumentList
examples.append(
- Example(
+ self.Example(
guid="%s-%s" % (set_type, i),
text=row.text,
- label=row.label if set_type != "test" else cls.LABELS[-1],
+ label=row.label if set_type != "test" else self.LABELS[-1],
)
)
return examples
diff --git a/jiant/tasks/lib/senteval/bigram_shift.py b/jiant/tasks/lib/senteval/bigram_shift.py
new file mode 100644
index 000000000..b212b54cd
--- /dev/null
+++ b/jiant/tasks/lib/senteval/bigram_shift.py
@@ -0,0 +1,35 @@
+from dataclasses import dataclass
+import jiant.tasks.lib.senteval.base as base
+from jiant.tasks.lib.templates.shared import labels_to_bimap
+
+
+@dataclass
+class Example(base.Example):
+ @property
+ def label_to_id(self):
+ return SentEvalBigramShiftTask.LABEL_TO_ID
+
+
+@dataclass
+class TokenizedExample(base.TokenizedExample):
+ pass
+
+
+@dataclass
+class DataRow(base.DataRow):
+ pass
+
+
+@dataclass
+class Batch(base.Batch):
+ pass
+
+
+class SentEvalBigramShiftTask(base.BaseSentEvalTask):
+ Example = Example
+ TokenizedExample = TokenizedExample
+ DataRow = DataRow
+ Batch = Batch
+
+ LABELS = ["I", "O"]
+ LABEL_TO_ID, ID_TO_LABEL = labels_to_bimap(LABELS)
diff --git a/jiant/tasks/lib/senteval/coordination_inversion.py b/jiant/tasks/lib/senteval/coordination_inversion.py
new file mode 100644
index 000000000..b4cae4a05
--- /dev/null
+++ b/jiant/tasks/lib/senteval/coordination_inversion.py
@@ -0,0 +1,35 @@
+from dataclasses import dataclass
+import jiant.tasks.lib.senteval.base as base
+from jiant.tasks.lib.templates.shared import labels_to_bimap
+
+
+@dataclass
+class Example(base.Example):
+ @property
+ def label_to_id(self):
+ return SentEvalCoordinationInversionTask.LABEL_TO_ID
+
+
+@dataclass
+class TokenizedExample(base.TokenizedExample):
+ pass
+
+
+@dataclass
+class DataRow(base.DataRow):
+ pass
+
+
+@dataclass
+class Batch(base.Batch):
+ pass
+
+
+class SentEvalCoordinationInversionTask(base.BaseSentEvalTask):
+ Example = Example
+ TokenizedExample = TokenizedExample
+ DataRow = DataRow
+ Batch = Batch
+
+ LABELS = ["I", "O"]
+ LABEL_TO_ID, ID_TO_LABEL = labels_to_bimap(LABELS)
diff --git a/jiant/tasks/lib/senteval/obj_number.py b/jiant/tasks/lib/senteval/obj_number.py
new file mode 100644
index 000000000..3d187f7fd
--- /dev/null
+++ b/jiant/tasks/lib/senteval/obj_number.py
@@ -0,0 +1,35 @@
+from dataclasses import dataclass
+import jiant.tasks.lib.senteval.base as base
+from jiant.tasks.lib.templates.shared import labels_to_bimap
+
+
+@dataclass
+class Example(base.Example):
+ @property
+ def label_to_id(self):
+ return SentEvalObjNumberTask.LABEL_TO_ID
+
+
+@dataclass
+class TokenizedExample(base.TokenizedExample):
+ pass
+
+
+@dataclass
+class DataRow(base.DataRow):
+ pass
+
+
+@dataclass
+class Batch(base.Batch):
+ pass
+
+
+class SentEvalObjNumberTask(base.BaseSentEvalTask):
+ Example = Example
+ TokenizedExample = TokenizedExample
+ DataRow = DataRow
+ Batch = Batch
+
+ LABELS = ["NN", "NNS"]
+ LABEL_TO_ID, ID_TO_LABEL = labels_to_bimap(LABELS)
diff --git a/jiant/tasks/lib/senteval/odd_man_out.py b/jiant/tasks/lib/senteval/odd_man_out.py
new file mode 100644
index 000000000..926f2f2a9
--- /dev/null
+++ b/jiant/tasks/lib/senteval/odd_man_out.py
@@ -0,0 +1,35 @@
+from dataclasses import dataclass
+import jiant.tasks.lib.senteval.base as base
+from jiant.tasks.lib.templates.shared import labels_to_bimap
+
+
+@dataclass
+class Example(base.Example):
+ @property
+ def label_to_id(self):
+ return SentEvalOddManOutTask.LABEL_TO_ID
+
+
+@dataclass
+class TokenizedExample(base.TokenizedExample):
+ pass
+
+
+@dataclass
+class DataRow(base.DataRow):
+ pass
+
+
+@dataclass
+class Batch(base.Batch):
+ pass
+
+
+class SentEvalOddManOutTask(base.BaseSentEvalTask):
+ Example = Example
+ TokenizedExample = TokenizedExample
+ DataRow = DataRow
+ Batch = Batch
+
+ LABELS = ["C", "O"]
+ LABEL_TO_ID, ID_TO_LABEL = labels_to_bimap(LABELS)
diff --git a/jiant/tasks/lib/senteval/past_present.py b/jiant/tasks/lib/senteval/past_present.py
new file mode 100644
index 000000000..5b09de51f
--- /dev/null
+++ b/jiant/tasks/lib/senteval/past_present.py
@@ -0,0 +1,35 @@
+from dataclasses import dataclass
+import jiant.tasks.lib.senteval.base as base
+from jiant.tasks.lib.templates.shared import labels_to_bimap
+
+
+@dataclass
+class Example(base.Example):
+ @property
+ def label_to_id(self):
+ return SentEvalPastPresentTask.LABEL_TO_ID
+
+
+@dataclass
+class TokenizedExample(base.TokenizedExample):
+ pass
+
+
+@dataclass
+class DataRow(base.DataRow):
+ pass
+
+
+@dataclass
+class Batch(base.Batch):
+ pass
+
+
+class SentEvalPastPresentTask(base.BaseSentEvalTask):
+ Example = Example
+ TokenizedExample = TokenizedExample
+ DataRow = DataRow
+ Batch = Batch
+
+ LABELS = ["PAST", "PRES"]
+ LABEL_TO_ID, ID_TO_LABEL = labels_to_bimap(LABELS)
diff --git a/jiant/tasks/lib/senteval/sentence_length.py b/jiant/tasks/lib/senteval/sentence_length.py
new file mode 100644
index 000000000..e8d3759e1
--- /dev/null
+++ b/jiant/tasks/lib/senteval/sentence_length.py
@@ -0,0 +1,35 @@
+from dataclasses import dataclass
+import jiant.tasks.lib.senteval.base as base
+from jiant.tasks.lib.templates.shared import labels_to_bimap
+
+
+@dataclass
+class Example(base.Example):
+ @property
+ def label_to_id(self):
+ return SentEvalSentenceLengthTask.LABEL_TO_ID
+
+
+@dataclass
+class TokenizedExample(base.TokenizedExample):
+ pass
+
+
+@dataclass
+class DataRow(base.DataRow):
+ pass
+
+
+@dataclass
+class Batch(base.Batch):
+ pass
+
+
+class SentEvalSentenceLengthTask(base.BaseSentEvalTask):
+ Example = Example
+ TokenizedExample = TokenizedExample
+ DataRow = DataRow
+ Batch = Batch
+
+ LABELS = [0, 1, 2, 3, 4, 5, 6]
+ LABEL_TO_ID, ID_TO_LABEL = labels_to_bimap(LABELS)
diff --git a/jiant/tasks/lib/senteval/subj_number.py b/jiant/tasks/lib/senteval/subj_number.py
new file mode 100644
index 000000000..f96e95866
--- /dev/null
+++ b/jiant/tasks/lib/senteval/subj_number.py
@@ -0,0 +1,35 @@
+from dataclasses import dataclass
+import jiant.tasks.lib.senteval.base as base
+from jiant.tasks.lib.templates.shared import labels_to_bimap
+
+
+@dataclass
+class Example(base.Example):
+ @property
+ def label_to_id(self):
+ return SentEvalSubjNumberTask.LABEL_TO_ID
+
+
+@dataclass
+class TokenizedExample(base.TokenizedExample):
+ pass
+
+
+@dataclass
+class DataRow(base.DataRow):
+ pass
+
+
+@dataclass
+class Batch(base.Batch):
+ pass
+
+
+class SentEvalSubjNumberTask(base.BaseSentEvalTask):
+ Example = Example
+ TokenizedExample = TokenizedExample
+ DataRow = DataRow
+ Batch = Batch
+
+ LABELS = ["NN", "NNS"]
+ LABEL_TO_ID, ID_TO_LABEL = labels_to_bimap(LABELS)
diff --git a/jiant/tasks/lib/senteval/top_constituents.py b/jiant/tasks/lib/senteval/top_constituents.py
new file mode 100644
index 000000000..29123fb38
--- /dev/null
+++ b/jiant/tasks/lib/senteval/top_constituents.py
@@ -0,0 +1,56 @@
+from dataclasses import dataclass
+import jiant.tasks.lib.senteval.base as base
+from jiant.tasks.lib.templates.shared import labels_to_bimap
+
+
+@dataclass
+class Example(base.Example):
+ @property
+ def label_to_id(self):
+ return SentEvalTopConstituentsTask.LABEL_TO_ID
+
+
+@dataclass
+class TokenizedExample(base.TokenizedExample):
+ pass
+
+
+@dataclass
+class DataRow(base.DataRow):
+ pass
+
+
+@dataclass
+class Batch(base.Batch):
+ pass
+
+
+class SentEvalTopConstituentsTask(base.BaseSentEvalTask):
+ Example = Example
+ TokenizedExample = TokenizedExample
+ DataRow = DataRow
+ Batch = Batch
+
+ LABELS = [
+ "ADVP_NP_VP_.",
+ "CC_ADVP_NP_VP_.",
+ "CC_NP_VP_.",
+ "IN_NP_VP_.",
+ "NP_ADVP_VP_.",
+ "NP_NP_VP_.",
+ "NP_PP_.",
+ "NP_VP_.",
+ "OTHER",
+ "PP_NP_VP_.",
+ "RB_NP_VP_.",
+ "SBAR_NP_VP_.",
+ "SBAR_VP_.",
+ "S_CC_S_.",
+ "S_NP_VP_.",
+ "S_VP_.",
+ "VBD_NP_VP_.",
+ "VP_.",
+ "WHADVP_SQ_.",
+ "WHNP_SQ_.",
+ ]
+ LABEL_TO_ID, ID_TO_LABEL = labels_to_bimap(LABELS)
diff --git a/jiant/tasks/lib/senteval/tree_depth.py b/jiant/tasks/lib/senteval/tree_depth.py
new file mode 100644
index 000000000..ba00dd183
--- /dev/null
+++ b/jiant/tasks/lib/senteval/tree_depth.py
@@ -0,0 +1,35 @@
+from dataclasses import dataclass
+import jiant.tasks.lib.senteval.base as base
+from jiant.tasks.lib.templates.shared import labels_to_bimap
+
+
+@dataclass
+class Example(base.Example):
+ @property
+ def label_to_id(self):
+ return SentEvalTreeDepthTask.LABEL_TO_ID
+
+
+@dataclass
+class TokenizedExample(base.TokenizedExample):
+ pass
+
+
+@dataclass
+class DataRow(base.DataRow):
+ pass
+
+
+@dataclass
+class Batch(base.Batch):
+ pass
+
+
+class SentEvalTreeDepthTask(base.BaseSentEvalTask):
+ Example = Example
+ TokenizedExample = TokenizedExample
+ DataRow = DataRow
+ Batch = Batch
+
+ LABELS = [5, 6, 7, 8, 9, 10, 11]
+ LABEL_TO_ID, ID_TO_LABEL = labels_to_bimap(LABELS)
diff --git a/jiant/tasks/lib/senteval/word_content.py b/jiant/tasks/lib/senteval/word_content.py
new file mode 100644
index 000000000..4ec253588
--- /dev/null
+++ b/jiant/tasks/lib/senteval/word_content.py
@@ -0,0 +1,1039 @@
+from dataclasses import dataclass
+import jiant.tasks.lib.senteval.base as base
+from jiant.tasks.lib.templates.shared import labels_to_bimap
+
+
+@dataclass
+class Example(base.Example):
+ @property
+ def label_to_id(self):
+ return SentEvalWordContentTask.LABEL_TO_ID
+
+
+@dataclass
+class TokenizedExample(base.TokenizedExample):
+ pass
+
+
+@dataclass
+class DataRow(base.DataRow):
+ pass
+
+
+@dataclass
+class Batch(base.Batch):
+ pass
+
+
+class SentEvalWordContentTask(base.BaseSentEvalTask):
+ Example = Example
+ TokenizedExample = TokenizedExample
+ DataRow = DataRow
+ Batch = Batch
+
+ # SentEval predicts the presence of words in a sentence with 1k-way classification.
+ # All examples satisfy:
+ # word in text.lower()
+ LABELS = [
+ "abandoned",
+ "abruptly",
+ "accent",
+ "access",
+ "according",
+ "account",
+ "ache",
+ "ached",
+ "acted",
+ "acting",
+ "actions",
+ "actual",
+ "address",
+ "advantage",
+ "advice",
+ "afford",
+ "agent",
+ "agreement",
+ "aiden",
+ "airport",
+ "alarm",
+ "albert",
+ "alert",
+ "alexander",
+ "alice",
+ "alley",
+ "allowing",
+ "aloud",
+ "amanda",
+ "amber",
+ "american",
+ "amused",
+ "amusement",
+ "ancient",
+ "andy",
+ "annie",
+ "announced",
+ "annoyed",
+ "answering",
+ "anticipation",
+ "anxious",
+ "anybody",
+ "apologize",
+ "appearance",
+ "appears",
+ "approach",
+ "approaching",
+ "arched",
+ "argue",
+ "argument",
+ "aria",
+ "arrive",
+ "ashe",
+ "assume",
+ "assured",
+ "attached",
+ "attacked",
+ "attempted",
+ "audience",
+ "available",
+ "awful",
+ "awkward",
+ "background",
+ "backpack",
+ "backward",
+ "backwards",
+ "bags",
+ "balance",
+ "bank",
+ "barn",
+ "bars",
+ "bastard",
+ "bath",
+ "beating",
+ "begged",
+ "begins",
+ "begun",
+ "behavior",
+ "bell",
+ "belly",
+ "belong",
+ "belonged",
+ "bench",
+ "biggest",
+ "bike",
+ "billy",
+ "bird",
+ "birds",
+ "birth",
+ "birthday",
+ "bitch",
+ "bitter",
+ "blank",
+ "blast",
+ "bleeding",
+ "blind",
+ "blocked",
+ "blond",
+ "blonde",
+ "bond",
+ "bone",
+ "bored",
+ "bothered",
+ "bound",
+ "bowed",
+ "bowl",
+ "boxes",
+ "branches",
+ "brave",
+ "bread",
+ "breast",
+ "breaths",
+ "brian",
+ "brick",
+ "brilliant",
+ "broad",
+ "brows",
+ "brush",
+ "brushing",
+ "buddy",
+ "build",
+ "buildings",
+ "bullet",
+ "bunch",
+ "butt",
+ "cage",
+ "cain",
+ "caleb",
+ "callum",
+ "calmly",
+ "cameron",
+ "capable",
+ "cards",
+ "career",
+ "carol",
+ "caroline",
+ "carriage",
+ "cash",
+ "cassidy",
+ "casual",
+ "catching",
+ "causing",
+ "centuries",
+ "chain",
+ "chairs",
+ "challenge",
+ "chamber",
+ "chances",
+ "changes",
+ "changing",
+ "charged",
+ "checking",
+ "chicken",
+ "chief",
+ "chill",
+ "chloe",
+ "chocolate",
+ "choked",
+ "chosen",
+ "christian",
+ "chuckle",
+ "cigarette",
+ "circles",
+ "circumstances",
+ "claim",
+ "claimed",
+ "clary",
+ "classes",
+ "claws",
+ "clay",
+ "cleaned",
+ "cleaning",
+ "clearing",
+ "clicked",
+ "cliff",
+ "climb",
+ "climbing",
+ "closely",
+ "closest",
+ "cloth",
+ "clothing",
+ "cloud",
+ "clue",
+ "clung",
+ "clutched",
+ "coach",
+ "cocked",
+ "code",
+ "colin",
+ "collapsed",
+ "collar",
+ "colors",
+ "commander",
+ "comment",
+ "complicated",
+ "concrete",
+ "condition",
+ "confidence",
+ "confident",
+ "confirmed",
+ "connected",
+ "connor",
+ "convince",
+ "convinced",
+ "cook",
+ "cops",
+ "copy",
+ "corners",
+ "correct",
+ "cost",
+ "courage",
+ "cousin",
+ "covering",
+ "covers",
+ "crack",
+ "cracked",
+ "crap",
+ "crash",
+ "crashed",
+ "crawled",
+ "cream",
+ "create",
+ "credit",
+ "crept",
+ "crime",
+ "crossing",
+ "crowded",
+ "cruel",
+ "crystal",
+ "cupped",
+ "curiosity",
+ "current",
+ "curse",
+ "cursed",
+ "cute",
+ "cutting",
+ "daemon",
+ "dagger",
+ "damned",
+ "damp",
+ "danced",
+ "danny",
+ "dante",
+ "darkened",
+ "darling",
+ "darted",
+ "dating",
+ "dave",
+ "dawn",
+ "dealing",
+ "delicate",
+ "delicious",
+ "della",
+ "demons",
+ "deny",
+ "department",
+ "desert",
+ "deserved",
+ "destroy",
+ "destroyed",
+ "detail",
+ "detective",
+ "devil",
+ "devon",
+ "digging",
+ "dining",
+ "direct",
+ "directions",
+ "disappear",
+ "disappointed",
+ "disappointment",
+ "disbelief",
+ "discuss",
+ "disgust",
+ "display",
+ "distant",
+ "distracted",
+ "dogs",
+ "dollars",
+ "double",
+ "doubted",
+ "dozen",
+ "drag",
+ "dragging",
+ "drake",
+ "drank",
+ "drawer",
+ "drinks",
+ "driveway",
+ "dropping",
+ "drops",
+ "drug",
+ "drugs",
+ "ducked",
+ "duke",
+ "dull",
+ "duncan",
+ "duty",
+ "dylan",
+ "eager",
+ "eased",
+ "east",
+ "eaten",
+ "echo",
+ "echoed",
+ "eddie",
+ "edges",
+ "elbow",
+ "elena",
+ "ellie",
+ "embarrassed",
+ "embrace",
+ "emerged",
+ "emergency",
+ "emotional",
+ "ends",
+ "enemies",
+ "enormous",
+ "envelope",
+ "equipment",
+ "evan",
+ "event",
+ "everybody",
+ "exact",
+ "exchange",
+ "exchanged",
+ "exclaimed",
+ "exhausted",
+ "exist",
+ "existed",
+ "existence",
+ "exit",
+ "expensive",
+ "experienced",
+ "explanation",
+ "exploded",
+ "exposed",
+ "extended",
+ "extremely",
+ "eyed",
+ "fabric",
+ "fade",
+ "fairly",
+ "faith",
+ "fake",
+ "falls",
+ "families",
+ "fangs",
+ "farm",
+ "farther",
+ "favor",
+ "feared",
+ "feed",
+ "fellow",
+ "fence",
+ "fierce",
+ "fifty",
+ "file",
+ "filling",
+ "fingertips",
+ "finn",
+ "fired",
+ "fixed",
+ "flame",
+ "flashlight",
+ "flicked",
+ "floating",
+ "flushed",
+ "foolish",
+ "football",
+ "footsteps",
+ "forcing",
+ "formed",
+ "frame",
+ "freedom",
+ "french",
+ "friday",
+ "friendly",
+ "frightened",
+ "frown",
+ "frowning",
+ "froze",
+ "frozen",
+ "frustrated",
+ "fuck",
+ "fucking",
+ "furious",
+ "furniture",
+ "fury",
+ "gabe",
+ "games",
+ "garage",
+ "garrett",
+ "gary",
+ "gasp",
+ "gates",
+ "gather",
+ "gathering",
+ "gavin",
+ "gear",
+ "gesture",
+ "gestured",
+ "ghost",
+ "girlfriend",
+ "glances",
+ "glare",
+ "glowing",
+ "goodbye",
+ "gorgeous",
+ "government",
+ "gown",
+ "grabbing",
+ "graham",
+ "grand",
+ "grandfather",
+ "grandma",
+ "grant",
+ "grasp",
+ "grave",
+ "greeted",
+ "grief",
+ "grinning",
+ "groan",
+ "growl",
+ "guessed",
+ "guest",
+ "guests",
+ "guns",
+ "gwen",
+ "halfway",
+ "hank",
+ "hanna",
+ "happily",
+ "happiness",
+ "harry",
+ "harsh",
+ "heal",
+ "heartbeat",
+ "heaven",
+ "height",
+ "hero",
+ "hesitation",
+ "hissed",
+ "hitting",
+ "holds",
+ "holiday",
+ "holly",
+ "holy",
+ "honor",
+ "hopefully",
+ "horizon",
+ "houses",
+ "humor",
+ "hundreds",
+ "hunger",
+ "hunt",
+ "hunting",
+ "hurting",
+ "ideas",
+ "idiot",
+ "ignoring",
+ "images",
+ "imagination",
+ "impressed",
+ "impression",
+ "inches",
+ "including",
+ "incredible",
+ "incredibly",
+ "informed",
+ "injured",
+ "inner",
+ "insane",
+ "intended",
+ "intensity",
+ "invisible",
+ "invited",
+ "iron",
+ "isaac",
+ "issue",
+ "items",
+ "jamie",
+ "jared",
+ "jealous",
+ "jeremy",
+ "jerk",
+ "jess",
+ "jesus",
+ "jimmy",
+ "joey",
+ "johnny",
+ "jonas",
+ "jordan",
+ "joseph",
+ "joshua",
+ "josie",
+ "journey",
+ "judge",
+ "jumping",
+ "kane",
+ "karen",
+ "katherine",
+ "katie",
+ "keeps",
+ "kevin",
+ "kicking",
+ "kidding",
+ "killer",
+ "kisses",
+ "knelt",
+ "knocking",
+ "ladies",
+ "landing",
+ "language",
+ "larger",
+ "lately",
+ "laura",
+ "lauren",
+ "lawyer",
+ "lean",
+ "leapt",
+ "learning",
+ "lesson",
+ "letters",
+ "licked",
+ "lies",
+ "lifting",
+ "lightning",
+ "likes",
+ "lined",
+ "liquid",
+ "lobby",
+ "location",
+ "london",
+ "lonely",
+ "louder",
+ "lover",
+ "loves",
+ "loving",
+ "lucien",
+ "lucy",
+ "major",
+ "mama",
+ "manner",
+ "marks",
+ "martin",
+ "mask",
+ "mason",
+ "mass",
+ "massive",
+ "mate",
+ "material",
+ "mattered",
+ "matters",
+ "meaning",
+ "meat",
+ "medical",
+ "melissa",
+ "member",
+ "members",
+ "mental",
+ "mere",
+ "merely",
+ "midnight",
+ "military",
+ "minds",
+ "miranda",
+ "mist",
+ "mixed",
+ "moaned",
+ "monday",
+ "moonlight",
+ "morgan",
+ "mortal",
+ "motioned",
+ "mountains",
+ "movements",
+ "moves",
+ "movies",
+ "mumbled",
+ "muscle",
+ "mystery",
+ "nails",
+ "named",
+ "nathan",
+ "nearest",
+ "necklace",
+ "needing",
+ "nerves",
+ "nervously",
+ "nicolas",
+ "nicole",
+ "nightmare",
+ "nights",
+ "nina",
+ "nodding",
+ "nods",
+ "nora",
+ "noted",
+ "notes",
+ "numbers",
+ "object",
+ "occurred",
+ "offering",
+ "officers",
+ "oliver",
+ "opens",
+ "opinion",
+ "option",
+ "orange",
+ "ordinary",
+ "original",
+ "ourselves",
+ "overhead",
+ "owner",
+ "packed",
+ "pages",
+ "painful",
+ "paint",
+ "painted",
+ "painting",
+ "palace",
+ "palms",
+ "paris",
+ "parted",
+ "particularly",
+ "partner",
+ "parts",
+ "passenger",
+ "passion",
+ "patch",
+ "patience",
+ "patient",
+ "patrick",
+ "patted",
+ "paying",
+ "period",
+ "permission",
+ "pete",
+ "photo",
+ "pile",
+ "pillow",
+ "pissed",
+ "pity",
+ "placing",
+ "plain",
+ "planet",
+ "plastic",
+ "pleasant",
+ "plus",
+ "pockets",
+ "points",
+ "polite",
+ "popped",
+ "positive",
+ "possibility",
+ "potential",
+ "pounded",
+ "powers",
+ "precious",
+ "prefer",
+ "prepare",
+ "president",
+ "preston",
+ "pretend",
+ "pretending",
+ "previous",
+ "price",
+ "pride",
+ "princess",
+ "prison",
+ "professor",
+ "program",
+ "progress",
+ "project",
+ "proof",
+ "proper",
+ "property",
+ "protection",
+ "protest",
+ "provide",
+ "provided",
+ "pulse",
+ "punch",
+ "punched",
+ "pure",
+ "purple",
+ "puts",
+ "quinn",
+ "quit",
+ "racing",
+ "radio",
+ "rafe",
+ "raise",
+ "raising",
+ "rapidly",
+ "rare",
+ "rarely",
+ "reaches",
+ "react",
+ "realizing",
+ "rear",
+ "rebecca",
+ "recall",
+ "received",
+ "recently",
+ "record",
+ "reflection",
+ "refuse",
+ "regular",
+ "reluctantly",
+ "remaining",
+ "remains",
+ "remembering",
+ "remind",
+ "remove",
+ "replaced",
+ "request",
+ "required",
+ "rescue",
+ "research",
+ "resist",
+ "responsibility",
+ "responsible",
+ "result",
+ "returning",
+ "reveal",
+ "revealed",
+ "revealing",
+ "ribs",
+ "riding",
+ "rifle",
+ "roar",
+ "robe",
+ "robert",
+ "rode",
+ "rope",
+ "rubbing",
+ "ruin",
+ "ruined",
+ "rule",
+ "runs",
+ "rushing",
+ "sadly",
+ "sadness",
+ "sake",
+ "sally",
+ "satisfaction",
+ "satisfied",
+ "saturday",
+ "savannah",
+ "saving",
+ "scanned",
+ "scar",
+ "scare",
+ "scattered",
+ "scott",
+ "scrambled",
+ "screams",
+ "seated",
+ "seats",
+ "secrets",
+ "section",
+ "sees",
+ "self",
+ "sell",
+ "sensation",
+ "sensed",
+ "senses",
+ "sentence",
+ "separate",
+ "serena",
+ "series",
+ "serve",
+ "served",
+ "settle",
+ "sexy",
+ "shade",
+ "shakes",
+ "shame",
+ "sharply",
+ "shattered",
+ "sheet",
+ "sheets",
+ "sheriff",
+ "shield",
+ "shining",
+ "shiver",
+ "shivered",
+ "shooting",
+ "shopping",
+ "shore",
+ "shortly",
+ "shorts",
+ "shout",
+ "shouting",
+ "shown",
+ "shrug",
+ "shuddered",
+ "sidewalk",
+ "sideways",
+ "signal",
+ "signs",
+ "silk",
+ "silly",
+ "similar",
+ "sisters",
+ "site",
+ "sits",
+ "skills",
+ "skirt",
+ "skull",
+ "slapped",
+ "sleeve",
+ "slide",
+ "slipping",
+ "slowed",
+ "slumped",
+ "smaller",
+ "smith",
+ "snap",
+ "social",
+ "society",
+ "sofa",
+ "somewhat",
+ "sooner",
+ "sophia",
+ "sophie",
+ "souls",
+ "sounding",
+ "source",
+ "spare",
+ "speech",
+ "spencer",
+ "spending",
+ "spinning",
+ "split",
+ "spring",
+ "squeeze",
+ "stands",
+ "stated",
+ "statement",
+ "states",
+ "steal",
+ "stepping",
+ "stiff",
+ "stirred",
+ "stole",
+ "stones",
+ "stops",
+ "straightened",
+ "stream",
+ "stretch",
+ "strike",
+ "strode",
+ "stroked",
+ "struggle",
+ "struggling",
+ "student",
+ "students",
+ "studying",
+ "stuffed",
+ "style",
+ "success",
+ "suggest",
+ "sunday",
+ "sunlight",
+ "supplies",
+ "surprisingly",
+ "surrounding",
+ "survived",
+ "suspect",
+ "suspected",
+ "suspicious",
+ "swallow",
+ "sweetheart",
+ "swiftly",
+ "swing",
+ "switch",
+ "t-shirt",
+ "tables",
+ "tail",
+ "tapped",
+ "tara",
+ "target",
+ "task",
+ "tasted",
+ "taught",
+ "taylor",
+ "teach",
+ "teacher",
+ "teased",
+ "teasing",
+ "television",
+ "tells",
+ "temper",
+ "temple",
+ "tense",
+ "tent",
+ "terms",
+ "terrified",
+ "terror",
+ "tessa",
+ "text",
+ "thigh",
+ "thighs",
+ "thousands",
+ "threatened",
+ "threatening",
+ "throughout",
+ "thrown",
+ "thrust",
+ "tighter",
+ "till",
+ "tipped",
+ "toby",
+ "toes",
+ "tony",
+ "tore",
+ "torn",
+ "total",
+ "tough",
+ "towel",
+ "tower",
+ "trace",
+ "tracks",
+ "traffic",
+ "trailed",
+ "trained",
+ "trap",
+ "trapped",
+ "traveled",
+ "travis",
+ "tray",
+ "treat",
+ "treated",
+ "trembled",
+ "trevor",
+ "trick",
+ "tristan",
+ "trunk",
+ "trusted",
+ "twelve",
+ "ugly",
+ "unconscious",
+ "unexpected",
+ "uniform",
+ "universe",
+ "unknown",
+ "unlike",
+ "unsure",
+ "unusual",
+ "upper",
+ "upright",
+ "upward",
+ "useful",
+ "useless",
+ "valley",
+ "vanished",
+ "various",
+ "vehicle",
+ "veins",
+ "victor",
+ "video",
+ "vincent",
+ "violet",
+ "visible",
+ "vulnerable",
+ "walks",
+ "wandered",
+ "warrior",
+ "wash",
+ "washed",
+ "waving",
+ "weather",
+ "werewolf",
+ "whipped",
+ "whispers",
+ "whom",
+ "willow",
+ "winced",
+ "wings",
+ "winked",
+ "wise",
+ "wishing",
+ "witch",
+ "wolves",
+ "worn",
+ "wounded",
+ "wounds",
+ "wrap",
+ "wrapping",
+ "wrists",
+ "wrote",
+ "xavier",
+ "yanked",
+ "yard",
+ "yards",
+ "yelling",
+ "zach",
+ ]
+ LABEL_TO_ID, ID_TO_LABEL = labels_to_bimap(LABELS)
diff --git a/jiant/tasks/lib/templates/squad_style/core.py b/jiant/tasks/lib/templates/squad_style/core.py
index b19e216f1..b57167e2b 100644
--- a/jiant/tasks/lib/templates/squad_style/core.py
+++ b/jiant/tasks/lib/templates/squad_style/core.py
@@ -5,7 +5,7 @@
from dataclasses import dataclass
from typing import Union, List, Dict, Optional
-from transformers.tokenization_bert import whitespace_tokenize
+from transformers.models.bert.tokenization_bert import whitespace_tokenize
from jiant.tasks.lib.templates.squad_style import utils as squad_utils
from jiant.shared.constants import PHASE
@@ -144,11 +144,13 @@ def to_feature_list(
# in the way they compute mask of added tokens.
tokenizer_type = type(tokenizer).__name__.replace("Tokenizer", "").lower()
sequence_added_tokens = (
- tokenizer.max_len - tokenizer.max_len_single_sentence + 1
+ tokenizer.model_max_length - tokenizer.model_max_length_single_sentence + 1
if tokenizer_type in MULTI_SEP_TOKENS_TOKENIZERS_SET
- else tokenizer.max_len - tokenizer.max_len_single_sentence
+ else tokenizer.model_max_length - tokenizer.model_max_length_single_sentence
+ )
+ sequence_pair_added_tokens = (
+ tokenizer.model_max_length - tokenizer.model_max_length_sentences_pair
)
- sequence_pair_added_tokens = tokenizer.max_len - tokenizer.max_len_sentences_pair
span_doc_tokens = all_doc_tokens
while len(spans) * doc_stride < len(all_doc_tokens):
diff --git a/jiant/tasks/lib/templates/squad_style/utils.py b/jiant/tasks/lib/templates/squad_style/utils.py
index bc84fc9eb..9cd35daa0 100644
--- a/jiant/tasks/lib/templates/squad_style/utils.py
+++ b/jiant/tasks/lib/templates/squad_style/utils.py
@@ -6,7 +6,7 @@
from dataclasses import dataclass
from typing import List, Dict
-from transformers.tokenization_bert import BasicTokenizer
+from transformers.models.bert.tokenization_bert import BasicTokenizer
from jiant.utils.display import maybe_tqdm
diff --git a/jiant/tasks/retrieval.py b/jiant/tasks/retrieval.py
index a4b378587..79719320d 100644
--- a/jiant/tasks/retrieval.py
+++ b/jiant/tasks/retrieval.py
@@ -3,6 +3,9 @@
from jiant.tasks.lib.abductive_nli import AbductiveNliTask
from jiant.tasks.lib.acceptability_judgement.definiteness import AcceptabilityDefinitenessTask
+from jiant.tasks.lib.acceptability_judgement.coord import AcceptabilityCoordTask
+from jiant.tasks.lib.acceptability_judgement.eos import AcceptabilityEOSTask
+from jiant.tasks.lib.acceptability_judgement.whwords import AcceptabilityWHwordsTask
from jiant.tasks.lib.adversarial_nli import AdversarialNliTask
from jiant.tasks.lib.arc_easy import ArcEasyTask
from jiant.tasks.lib.arc_challenge import ArcChallengeTask
@@ -50,7 +53,6 @@
from jiant.tasks.lib.rte import RteTask
from jiant.tasks.lib.ropes import RopesTask
from jiant.tasks.lib.scitail import SciTailTask
-from jiant.tasks.lib.senteval.tense import SentevalTenseTask
from jiant.tasks.lib.edge_probing.semeval import SemevalTask
from jiant.tasks.lib.snli import SnliTask
from jiant.tasks.lib.socialiqa import SocialIQATask
@@ -58,6 +60,16 @@
from jiant.tasks.lib.edge_probing.spr2 import Spr2Task
from jiant.tasks.lib.squad import SquadTask
from jiant.tasks.lib.edge_probing.srl import SrlTask
+from jiant.tasks.lib.senteval.bigram_shift import SentEvalBigramShiftTask
+from jiant.tasks.lib.senteval.coordination_inversion import SentEvalCoordinationInversionTask
+from jiant.tasks.lib.senteval.obj_number import SentEvalObjNumberTask
+from jiant.tasks.lib.senteval.odd_man_out import SentEvalOddManOutTask
+from jiant.tasks.lib.senteval.past_present import SentEvalPastPresentTask
+from jiant.tasks.lib.senteval.sentence_length import SentEvalSentenceLengthTask
+from jiant.tasks.lib.senteval.subj_number import SentEvalSubjNumberTask
+from jiant.tasks.lib.senteval.top_constituents import SentEvalTopConstituentsTask
+from jiant.tasks.lib.senteval.tree_depth import SentEvalTreeDepthTask
+from jiant.tasks.lib.senteval.word_content import SentEvalWordContentTask
from jiant.tasks.lib.sst import SstTask
from jiant.tasks.lib.stsb import StsbTask
from jiant.tasks.lib.superglue_axg import SuperglueWinogenderDiagnosticsTask
@@ -86,6 +98,9 @@
"arc_challenge": ArcChallengeTask,
"superglue_axg": SuperglueWinogenderDiagnosticsTask,
"acceptability_definiteness": AcceptabilityDefinitenessTask,
+ "acceptability_coord": AcceptabilityCoordTask,
+ "acceptability_eos": AcceptabilityEOSTask,
+ "acceptability_whwords": AcceptabilityWHwordsTask,
"adversarial_nli": AdversarialNliTask,
"boolq": BoolQTask,
"bucc2018": Bucc2018Task,
@@ -131,8 +146,17 @@
"ropes": RopesTask,
"rte": RteTask,
"scitail": SciTailTask,
- "senteval_tense": SentevalTenseTask,
"semeval": SemevalTask,
+ "senteval_bigram_shift": SentEvalBigramShiftTask,
+ "senteval_coordination_inversion": SentEvalCoordinationInversionTask,
+ "senteval_obj_number": SentEvalObjNumberTask,
+ "senteval_odd_man_out": SentEvalOddManOutTask,
+ "senteval_past_present": SentEvalPastPresentTask,
+ "senteval_sentence_length": SentEvalSentenceLengthTask,
+ "senteval_subj_number": SentEvalSubjNumberTask,
+ "senteval_top_constituents": SentEvalTopConstituentsTask,
+ "senteval_tree_depth": SentEvalTreeDepthTask,
+ "senteval_word_content": SentEvalWordContentTask,
"snli": SnliTask,
"socialiqa": SocialIQATask,
"spr1": Spr1Task,
diff --git a/jiant/utils/transformer_utils.py b/jiant/utils/transformer_utils.py
deleted file mode 100644
index 844b6c368..000000000
--- a/jiant/utils/transformer_utils.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import contextlib
-
-from jiant.shared.model_resolution import ModelArchitectures
-
-
-@contextlib.contextmanager
-def output_hidden_states_context(encoder):
- model_arch = ModelArchitectures.from_encoder(encoder)
- if model_arch in (
- ModelArchitectures.BERT,
- ModelArchitectures.ROBERTA,
- ModelArchitectures.ALBERT,
- ModelArchitectures.XLM_ROBERTA,
- ModelArchitectures.ELECTRA,
- ):
- if hasattr(encoder.encoder, "output_hidden_states"):
- # Transformers < v2
- modified_obj = encoder.encoder
- elif hasattr(encoder.encoder.config, "output_hidden_states"):
- # Transformers >= v3
- modified_obj = encoder.encoder.config
- else:
- raise RuntimeError(f"Failed to convert model {type(encoder)} to output hidden states")
- old_value = modified_obj.output_hidden_states
- modified_obj.output_hidden_states = True
- yield
- modified_obj.output_hidden_states = old_value
- elif model_arch in (ModelArchitectures.BART, ModelArchitectures.MBART):
- yield
- return
- else:
- raise KeyError(model_arch)
diff --git a/requirements-no-torch.txt b/requirements-no-torch.txt
index 43b9b2bd2..c9257de9f 100644
--- a/requirements-no-torch.txt
+++ b/requirements-no-torch.txt
@@ -13,6 +13,6 @@ seqeval==0.0.12
scikit-learn==0.22.2.post1
scipy==1.4.1
sentencepiece==0.1.86
-tokenizers==0.8.1.rc2
+tokenizers==0.9.4
tqdm==4.46.0
-transformers==3.1.0
+transformers==4.3.3
diff --git a/setup.py b/setup.py
index ed46c0070..31f16f366 100644
--- a/setup.py
+++ b/setup.py
@@ -72,10 +72,10 @@
"scikit-learn == 0.22.2.post1",
"scipy == 1.4.1",
"sentencepiece == 0.1.86",
- "tokenizers == 0.8.1.rc2",
+ "tokenizers == 0.9.4",
"torch >= 1.5.0",
"tqdm == 4.46.0",
- "transformers == 3.1.0",
+ "transformers == 4.3.3",
"torchvision == 0.6.0",
],
extras_require=extras,
diff --git a/tests/proj/simple/test_runscript.py b/tests/proj/simple/test_runscript.py
index a17da99f2..a79a975b3 100644
--- a/tests/proj/simple/test_runscript.py
+++ b/tests/proj/simple/test_runscript.py
@@ -8,6 +8,7 @@
import jiant.utils.torch_utils as torch_utils
+@pytest.mark.gpu
@pytest.mark.parametrize("task_name", ["copa"])
@pytest.mark.parametrize("model_type", ["bert-base-cased"])
def test_simple_runscript(tmpdir, task_name, model_type):