From 06b60c8b05fa2753098385c4bc2a8b30bfc391a7 Mon Sep 17 00:00:00 2001 From: flozi00 Date: Tue, 23 Jun 2020 00:40:19 +0200 Subject: [PATCH] [Modelcard] bart-squadv2 (#5011) * [Modelcard] bart-squadv2 * Update README.md * Update README.md --- model_cards/a-ware/bart-squadv2/README.md | 76 +++++++++++++++++++++++ 1 file changed, 76 insertions(+) create mode 100644 model_cards/a-ware/bart-squadv2/README.md diff --git a/model_cards/a-ware/bart-squadv2/README.md b/model_cards/a-ware/bart-squadv2/README.md new file mode 100644 index 00000000000..a6e088c7214 --- /dev/null +++ b/model_cards/a-ware/bart-squadv2/README.md @@ -0,0 +1,76 @@ +--- +datasets: +- squad_v2 +--- + +# BART-LARGE finetuned on SQuADv2 + +This is bart-large model finetuned on SQuADv2 dataset for question answering task + +## Model details +BART was propsed in the [paper](https://arxiv.org/abs/1910.13461) **BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension**. +BART is a seq2seq model intended for both NLG and NLU tasks. + +To use BART for question answering tasks, we feed the complete document into the encoder and decoder, and use the top +hidden state of the decoder as a representation for each +word. This representation is used to classify the token. As given in the paper bart-large achives comparable to ROBERTa on SQuAD. +Another notable thing about BART is that it can handle sequences with upto 1024 tokens. + +| Param | #Value | +|---------------------|--------| +| encoder layers | 12 | +| decoder layers | 12 | +| hidden size | 4096 | +| num attetion heads | 16 | +| on disk size | 1.63GB | + + +## Model training +This model was trained with following parameters using simpletransformers wrapper: +``` +train_args = { + 'learning_rate': 1e-5, + 'max_seq_length': 512, + 'doc_stride': 512, + 'overwrite_output_dir': True, + 'reprocess_input_data': False, + 'train_batch_size': 8, + 'num_train_epochs': 2, + 'gradient_accumulation_steps': 2, + 'no_cache': True, + 'use_cached_eval_features': False, + 'save_model_every_epoch': False, + 'output_dir': "bart-squadv2", + 'eval_batch_size': 32, + 'fp16_opt_level': 'O2', + } +``` + +[You can even train your own model using this colab notebook](https://colab.research.google.com/drive/1I5cK1M_0dLaf5xoewh6swcm5nAInfwHy?usp=sharing) + +## Results +```{"correct": 6832, "similar": 4409, "incorrect": 632, "eval_loss": -14.950117511952177}``` + +## Model in Action 🚀 +```python3 +from transformers import BartTokenizer, BartForQuestionAnswering +import torch + +tokenizer = BartTokenizer.from_pretrained('a-ware/bart-squadv2') +model = BartForQuestionAnswering.from_pretrained('a-ware/bart-squadv2') + +question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet" +encoding = tokenizer.encode_plus(question, text, return_tensors='pt') +input_ids = encoding['input_ids'] +attention_mask = encoding['attention_mask'] + +start_scores, end_scores = model(input_ids, attention_mask=attention_mask, output_attentions=False)[:2] + +all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0]) +answer = ' '.join(all_tokens[torch.argmax(start_scores) : torch.argmax(end_scores)+1]) +answer = tokenizer.convert_tokens_to_ids(answer.split()) +answer = tokenizer.decode(answer) +#answer => 'a nice puppet' +``` + +> Created with ❤️ by A-ware UG [![Github icon](https://cdn0.iconfinder.com/data/icons/octicons/1024/mark-github-32.png)](https://github.com/aware-ai)