--- language: - en license: mit library_name: transformers tags: - deberta - deberta-v3 - question-answering - squad - squad_v2 - lora - peft datasets: - squad_v2 - squad base_model: microsoft/deberta-v3-large model-index: - name: sjrhuschlee/deberta-v3-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: 87.956 name: Exact Match - type: f1 value: 90.776 name: F1 - task: type: question-answering name: Question Answering dataset: name: squad type: squad config: plain_text split: validation metrics: - type: exact_match value: 89.29 name: Exact Match - type: f1 value: 94.985 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: 31.167 name: Exact Match - type: f1 value: 41.787 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: 75.993 name: Exact Match - type: f1 value: 80.495 name: F1 - task: type: question-answering name: Question Answering dataset: name: squadshifts type: squadshifts config: amazon split: test metrics: - type: exact_match value: 66.272 name: Exact Match - type: f1 value: 77.941 name: F1 - task: type: question-answering name: Question Answering dataset: name: squadshifts type: squadshifts config: new_wiki split: test metrics: - type: exact_match value: 81.456 name: Exact Match - type: f1 value: 89.142 name: F1 - task: type: question-answering name: Question Answering dataset: name: squadshifts type: squadshifts config: nyt split: test metrics: - type: exact_match value: 81.739 name: Exact Match - type: f1 value: 88.826 name: F1 - task: type: question-answering name: Question Answering dataset: name: squadshifts type: squadshifts config: reddit split: test metrics: - type: exact_match value: 61.4 name: Exact Match - type: f1 value: 69.999 name: F1 --- # deberta-v3-large for Extractive QA This is the [deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-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. This model was trained using LoRA available through the [PEFT library](https://github.com/huggingface/peft). ## Overview **Language model:** deberta-v3-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/deberta-v3-large-squad2" # a) Using pipelines nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) qa_input = { 'question': '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) tokenizer = AutoTokenizer.from_pretrained(model_name) question = 'Where do I live?' context = 'My name is Sarah and I live in London' encoding = tokenizer(question, context, return_tensors="pt") start_scores, end_scores = model( encoding["input_ids"], attention_mask=encoding["attention_mask"], return_dict=False ) all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0].tolist()) answer_tokens = all_tokens[torch.argmax(start_scores):torch.argmax(end_scores) + 1] answer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens)) # 'London' ``` ## Metrics ```bash # Squad v2 { "eval_HasAns_exact": 84.83468286099865, "eval_HasAns_f1": 90.48374860633226, "eval_HasAns_total": 5928, "eval_NoAns_exact": 91.0681244743482, "eval_NoAns_f1": 91.0681244743482, "eval_NoAns_total": 5945, "eval_best_exact": 87.95586625115808, "eval_best_exact_thresh": 0.0, "eval_best_f1": 90.77635490089573, "eval_best_f1_thresh": 0.0, "eval_exact": 87.95586625115808, "eval_f1": 90.77635490089592, "eval_runtime": 623.1333, "eval_samples": 11951, "eval_samples_per_second": 19.179, "eval_steps_per_second": 0.799, "eval_total": 11873 } # Squad { "eval_exact_match": 89.29044465468307, "eval_f1": 94.9846365606959, "eval_runtime": 553.7132, "eval_samples": 10618, "eval_samples_per_second": 19.176, "eval_steps_per_second": 0.8 } ``` ### 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/deberta-v3-large-squad2" ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 1 - total_train_batch_size: 24 - 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 ### LoRA Config ``` { "base_model_name_or_path": "microsoft/deberta-v3-large", "bias": "none", "fan_in_fan_out": false, "inference_mode": true, "init_lora_weights": true, "lora_alpha": 32, "lora_dropout": 0.1, "modules_to_save": ["qa_outputs"], "peft_type": "LORA", "r": 8, "target_modules": [ "query_proj", "key_proj", "value_proj", "dense" ], "task_type": "QUESTION_ANS" } ``` ### Framework versions - Transformers 4.30.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3