--- language: - en license: mit library_name: transformers tags: - question-answering - squad - squad_v2 - t5 - lora - peft datasets: - squad_v2 - squad base_model: google/flan-t5-large model-index: - name: sjrhuschlee/flan-t5-large-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: 86.819 name: Exact Match - type: f1 value: 89.569 name: F1 - task: type: question-answering name: Question Answering dataset: name: squad type: squad config: plain_text split: validation metrics: - type: exact_match value: 89.357 name: Exact Match - type: f1 value: 95.060 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: 48.833 name: Exact Match - type: f1 value: 62.555 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: 84.835 name: Exact Match - type: f1 value: 90.245 name: F1 - task: type: question-answering name: Question Answering dataset: name: squadshifts amazon type: squadshifts config: amazon split: test metrics: - type: exact_match value: 76.722 name: Exact Match - type: f1 value: 89.680 name: F1 - task: type: question-answering name: Question Answering dataset: name: squadshifts new_wiki type: squadshifts config: new_wiki split: test metrics: - type: exact_match value: 84.316 name: Exact Match - type: f1 value: 92.967 name: F1 - task: type: question-answering name: Question Answering dataset: name: squadshifts nyt type: squadshifts config: nyt split: test metrics: - type: exact_match value: 86.925 name: Exact Match - type: f1 value: 94.064 name: F1 - task: type: question-answering name: Question Answering dataset: name: squadshifts reddit type: squadshifts config: reddit split: test metrics: - type: exact_match value: 78.241 name: Exact Match - type: f1 value: 89.243 name: F1 --- # flan-t5-large for Extractive QA This is the [flan-t5-large](https://huggingface.co/google/flan-t5-large) 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. This model was trained using LoRA available through the [PEFT library](https://github.com/huggingface/peft). **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-large **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Infrastructure**: 1x NVIDIA 3070 ## Model Usage ### Using Transformers This uses the merged weights (base model weights + LoRA weights) to allow for simple use in Transformers pipelines. It has the same performance as using the weights separately when using the PEFT library. ```python import torch from transformers import( AutoModelForQuestionAnswering, AutoTokenizer, pipeline ) model_name = "sjrhuschlee/flan-t5-large-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': 0.984, '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": 85.08771929824562, "eval_HasAns_f1": 90.598422845031, "eval_HasAns_total": 5928, "eval_NoAns_exact": 88.47771236333053, "eval_NoAns_f1": 88.47771236333053, "eval_NoAns_total": 5945, "eval_best_exact": 86.78514276088605, "eval_best_exact_thresh": 0.0, "eval_best_f1": 89.53654936623764, "eval_best_f1_thresh": 0.0, "eval_exact": 86.78514276088605, "eval_f1": 89.53654936623776, "eval_runtime": 1908.3189, "eval_samples": 12001, "eval_samples_per_second": 6.289, "eval_steps_per_second": 0.787, "eval_total": 11873 } # Squad { "eval_HasAns_exact": 85.99810785241249, "eval_HasAns_f1": 91.296119057944, "eval_HasAns_total": 10570, "eval_best_exact": 85.99810785241249, "eval_best_exact_thresh": 0.0, "eval_best_f1": 91.296119057944, "eval_best_f1_thresh": 0.0, "eval_exact": 85.99810785241249, "eval_f1": 91.296119057944, "eval_runtime": 1508.9596, "eval_samples": 10657, "eval_samples_per_second": 7.062, "eval_steps_per_second": 0.883, "eval_total": 10570 } ``` ### Using with Peft **NOTE**: This requires code in the PR https://github.com/huggingface/peft/pull/473 for the PEFT library. ```python #!pip install peft from peft import LoraConfig, PeftModelForQuestionAnswering from transformers import AutoModelForQuestionAnswering, AutoTokenizer model_name = "sjrhuschlee/flan-t5-large-squad2" ```