| Quantization made by Richard Erkhov. | |
| [Github](https://github.com/RichardErkhov) | |
| [Discord](https://discord.gg/pvy7H8DZMG) | |
| [Request more models](https://github.com/RichardErkhov/quant_request) | |
| bart-squadv2 - bnb 4bits | |
| - Model creator: https://huggingface.co/aware-ai/ | |
| - Original model: https://huggingface.co/aware-ai/bart-squadv2/ | |
| Original model description: | |
| --- | |
| 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(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 [](https://github.com/aware-ai) | |