Model card: GPT-2 fine-tuned on CommonGen (huggingface#8248)

model_cards/mrm8488/GPT-2-finetuned-common_gen/README.md

@@ -0,0 +1,63 @@
+---
+language: en
+datasets:
+- common_gen
+widget:
+- text: "<|endoftext|> apple, tree, pick:"
+---
+
+# GPT-2 fine-tuned on CommonGen
+
+[GPT-2](https://huggingface.co/gpt2) fine-tuned on [CommonGen](https://inklab.usc.edu/CommonGen/index.html) for *Generative Commonsense Reasoning*.
+
+## Details of GPT-2
+
+GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This
+means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots
+of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
+it was trained to guess the next word in sentences.
+
+More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
+shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
+predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
+
+This way, the model learns an inner representation of the English language that can then be used to extract features
+useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a
+prompt.
+
+
+## Details of the dataset 📚 
+
+CommonGen is a constrained text generation task, associated with a benchmark dataset, to explicitly test machines for the ability of generative commonsense reasoning. Given a set of common concepts; the task is to generate a coherent sentence describing an everyday scenario using these concepts.
+
+CommonGen is challenging because it inherently requires 1) relational reasoning using background commonsense knowledge, and 2) compositional generalization ability to work on unseen concept combinations. Our dataset, constructed through a combination of crowd-sourcing from AMT and existing caption corpora, consists of 30k concept-sets and 50k sentences in total.
+
+
+| Dataset  | Split | # samples |
+| -------- | ----- | --------- |
+| common_gen | train  | 67389   |
+| common_gen | valid  | 4018    |
+| common_gen | test   | 1497    |
+
+
+## Model fine-tuning 🏋️‍
+
+You can find the fine-tuning script [here](https://github.com/huggingface/transformers/tree/master/examples/language-modeling)
+
+## Model in Action 🚀
+
+```bash
+python ./transformers/examples/text-generation/run_generation.py \
+    --model_type=gpt2 \
+    --model_name_or_path="mrm8488/GPT-2-finetuned-common_gen" \
+    --num_return_sequences 1 \
+    --prompt "<|endoftext|> kid, room, dance:" \
+    --stop_token "."
+```
+
+> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
+
+> Made with <span style="color: #e25555;">&hearts;</span> in Spain
+
+
+