Merge pull request huggingface#9 from ROCmSoftwarePlatform/bert-tf2

Bert tf2

scripts/bert/bert_train.py

@@ -0,0 +1,37 @@
+import tensorflow as tf
+from datasets import load_dataset, load_metric
+from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
+
+strategy = tf.distribute.MirroredStrategy(devices=["GPU:0", "GPU:1", "GPU:2", "GPU:3"])
+with strategy.scope():
+    raw_datasets = load_dataset("imdb")
+    tokenizer = AutoTokenizer.from_pretrained("bert-large-uncased")
+
+    def tokenize_function(examples):
+        return tokenizer(examples["text"], padding="max_length", truncation=True)
+
+    tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)
+
+    small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000))
+    small_eval_dataset  = tokenized_datasets["test"].shuffle(seed=42).select(range(1000))
+
+    model = TFAutoModelForSequenceClassification.from_pretrained("bert-large-uncased", num_labels=2)
+    tf_train_dataset = small_train_dataset.remove_columns(["text"]).with_format("tensorflow")
+    tf_eval_dataset  = small_eval_dataset.remove_columns(["text"]).with_format("tensorflow")
+
+    train_features = {x: tf_train_dataset[x] for x in tokenizer.model_input_names}
+    train_tf_dataset = tf.data.Dataset.from_tensor_slices((train_features, tf_train_dataset["label"]))
+    train_tf_dataset = train_tf_dataset.shuffle(len(tf_train_dataset)).batch(8)
+
+    eval_features = {x: tf_eval_dataset[x] for x in tokenizer.model_input_names}
+    eval_tf_dataset = tf.data.Dataset.from_tensor_slices((eval_features, tf_eval_dataset["label"]))
+    eval_tf_dataset = eval_tf_dataset.batch(8)
+
+    model.compile(
+        optimizer=tf.keras.optimizers.Adam(learning_rate=5e-5),
+        loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+        metrics=tf.metrics.SparseCategoricalAccuracy(),
+    )
+
+print("==================================== Evaluating Model =================================")
+model.fit(train_tf_dataset, validation_data=eval_tf_dataset, epochs=3)

scripts/bert/bert_train.sh

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+#!/bin/bash
+set -e 
+set -x
+pip3 install transformers datasets
+
+
+cd ~ && git clone --branch bert-tf2 https://github.com/ROCmSoftwarePlatform/transformers
+# Script to train the small 117M model
+python3 transformers/scripts/bert/bert_train.py > log.txt
+cat log.txt | tail -n 1
+cat log.txt | tail -n 1 | awk '{ print "Accuracy: " $(NF) }'
+