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README.md

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+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 [![Github icon](https://cdn0.iconfinder.com/data/icons/octicons/1024/mark-github-32.png)](https://github.com/aware-ai)
+
+