--- license: mit datasets: - squad_v2 - squad language: - en library_name: transformers pipeline_tag: question-answering tags: - question-answering - squad - squad_v2 - t5 model-index: - name: sjrhuschlee/flan-t5-base-squad2 results: - task: type: question-answering name: Question Answering dataset: name: squad_v2 type: squad_v2 config: squad_v2 split: validation metrics: - type: exact_match value: 82.203 name: Exact Match - type: f1 value: 85.283 name: F1 - task: type: question-answering name: Question Answering dataset: name: squad type: squad config: plain_text split: validation metrics: - type: exact_match value: 86.320 name: Exact Match - type: f1 value: 92.947 name: F1 - task: type: question-answering name: Question Answering dataset: name: adversarial_qa type: adversarial_qa config: adversarialQA split: validation metrics: - type: exact_match value: 23.133 name: Exact Match - type: f1 value: 31.386 name: F1 - task: type: question-answering name: Question Answering dataset: name: squad_adversarial type: squad_adversarial config: AddOneSent split: validation metrics: - type: exact_match value: 68.159 name: Exact Match - type: f1 value: 71.876 name: F1 - task: type: question-answering name: Question Answering dataset: name: squadshifts type: squadshifts config: amazon split: test metrics: - type: exact_match value: 67.587 name: Exact Match - type: f1 value: 80.085 name: F1 - task: type: question-answering name: Question Answering dataset: name: squadshifts type: squadshifts config: new_wiki split: test metrics: - type: exact_match value: 77.261 name: Exact Match - type: f1 value: 85.068 name: F1 - task: type: question-answering name: Question Answering dataset: name: squadshifts type: squadshifts config: nyt split: test metrics: - type: exact_match value: 79.066 name: Exact Match - type: f1 value: 86.178 name: F1 - task: type: question-answering name: Question Answering dataset: name: squadshifts type: squadshifts config: reddit split: test metrics: - type: exact_match value: 65.052 name: Exact Match - type: f1 value: 74.785 name: F1 --- # flan-t5-base for Extractive QA This is the [flan-t5-base](https://huggingface.co/google/flan-t5-base) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Extractive Question Answering. **UPDATE:** With transformers version 4.31.0 the `use_remote_code=True` is no longer necessary and if used will cause `AutoModelForQuestionAnswering.from_pretrained()` to not work properly. **NOTE:** The `` token must be manually added to the beginning of the question for this model to work properly. It uses the `` token to be able to make "no answer" predictions. The t5 tokenizer does not automatically add this special token which is why it is added manually. ## Overview **Language model:** flan-t5-base **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Infrastructure**: 1x NVIDIA 3070 ## Model Usage ```python import torch from transformers import( AutoModelForQuestionAnswering, AutoTokenizer, pipeline ) model_name = "sjrhuschlee/flan-t5-base-squad2" # a) Using pipelines nlp = pipeline( 'question-answering', model=model_name, tokenizer=model_name, # trust_remote_code=True, # Do not use if version transformers>=4.31.0 ) qa_input = { 'question': f'{nlp.tokenizer.cls_token}Where do I live?', # 'Where do I live?' 'context': 'My name is Sarah and I live in London' } res = nlp(qa_input) # {'score': 0.980, 'start': 30, 'end': 37, 'answer': ' London'} # b) Load model & tokenizer model = AutoModelForQuestionAnswering.from_pretrained( model_name, # trust_remote_code=True # Do not use if version transformers>=4.31.0 ) tokenizer = AutoTokenizer.from_pretrained(model_name) question = f'{tokenizer.cls_token}Where do I live?' # 'Where do I live?' context = 'My name is Sarah and I live in London' encoding = tokenizer(question, context, return_tensors="pt") output = model( encoding["input_ids"], attention_mask=encoding["attention_mask"] ) all_tokens = tokenizer.convert_ids_to_tokens(encoding["input_ids"][0].tolist()) answer_tokens = all_tokens[torch.argmax(output["start_logits"]):torch.argmax(output["end_logits"]) + 1] answer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens)) # 'London' ``` ## Metrics ```bash # Squad v2 { "eval_HasAns_exact": 79.97638326585695, "eval_HasAns_f1": 86.1444296592862, "eval_HasAns_total": 5928, "eval_NoAns_exact": 84.42388561816652, "eval_NoAns_f1": 84.42388561816652, "eval_NoAns_total": 5945, "eval_best_exact": 82.2033184536343, "eval_best_exact_thresh": 0.0, "eval_best_f1": 85.28292588395921, "eval_best_f1_thresh": 0.0, "eval_exact": 82.2033184536343, "eval_f1": 85.28292588395928, "eval_runtime": 522.0299, "eval_samples": 12001, "eval_samples_per_second": 22.989, "eval_steps_per_second": 0.96, "eval_total": 11873 } # Squad { "eval_exact_match": 86.3197729422895, "eval_f1": 92.94686836210295, "eval_runtime": 442.1088, "eval_samples": 10657, "eval_samples_per_second": 24.105, "eval_steps_per_second": 1.007 } ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 6 - total_train_batch_size: 96 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4.0 ### Training results ### Framework versions - Transformers 4.30.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3