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--- |
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language: |
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- en |
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license: mit |
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library_name: transformers |
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tags: |
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- deberta |
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- deberta-v3 |
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- question-answering |
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- squad |
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- squad_v2 |
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- lora |
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- peft |
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datasets: |
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- squad_v2 |
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- squad |
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base_model: microsoft/deberta-v3-large |
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model-index: |
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- name: sjrhuschlee/deberta-v3-large-squad2 |
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results: |
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- task: |
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type: question-answering |
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name: Question Answering |
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dataset: |
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name: squad_v2 |
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type: squad_v2 |
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config: squad_v2 |
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split: validation |
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metrics: |
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- type: exact_match |
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value: 87.956 |
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name: Exact Match |
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- type: f1 |
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value: 90.781 |
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name: F1 |
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- task: |
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type: question-answering |
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name: Question Answering |
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dataset: |
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name: squad |
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type: squad |
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config: plain_text |
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split: validation |
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metrics: |
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- type: exact_match |
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value: 89.290 |
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name: Exact Match |
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- type: f1 |
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value: 95.008 |
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name: F1 |
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- task: |
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type: question-answering |
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name: Question Answering |
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dataset: |
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name: adversarial_qa |
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type: adversarial_qa |
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config: adversarialQA |
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split: validation |
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metrics: |
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- type: exact_match |
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value: 41.400 |
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name: Exact Match |
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- type: f1 |
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value: 55.676 |
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name: F1 |
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- task: |
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type: question-answering |
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name: Question Answering |
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dataset: |
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name: squad_adversarial |
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type: squad_adversarial |
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config: AddOneSent |
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split: validation |
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metrics: |
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- type: exact_match |
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value: 83.660 |
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name: Exact Match |
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- type: f1 |
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value: 89.451 |
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name: F1 |
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- task: |
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type: question-answering |
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name: Question Answering |
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dataset: |
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name: squadshifts amazon |
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type: squadshifts |
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config: amazon |
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split: test |
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metrics: |
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- type: exact_match |
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value: 74.487 |
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name: Exact Match |
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- type: f1 |
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value: 87.745 |
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name: F1 |
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- task: |
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type: question-answering |
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name: Question Answering |
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dataset: |
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name: squadshifts new_wiki |
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type: squadshifts |
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config: new_wiki |
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split: test |
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metrics: |
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- type: exact_match |
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value: 84.782 |
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name: Exact Match |
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- type: f1 |
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value: 93.114 |
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name: F1 |
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- task: |
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type: question-answering |
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name: Question Answering |
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dataset: |
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name: squadshifts nyt |
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type: squadshifts |
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config: nyt |
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split: test |
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metrics: |
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- type: exact_match |
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value: 85.643 |
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name: Exact Match |
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- type: f1 |
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value: 93.258 |
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name: F1 |
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- task: |
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type: question-answering |
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name: Question Answering |
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dataset: |
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name: squadshifts reddit |
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type: squadshifts |
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config: reddit |
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split: test |
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metrics: |
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- type: exact_match |
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value: 74.702 |
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name: Exact Match |
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- type: f1 |
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value: 85.861 |
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name: F1 |
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--- |
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# deberta-v3-large for Extractive QA |
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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. |
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This model was trained using LoRA available through the [PEFT library](https://github.com/huggingface/peft). |
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## Overview |
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**Language model:** deberta-v3-large |
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**Language:** English |
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**Downstream-task:** Extractive QA |
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**Training data:** SQuAD 2.0 |
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**Eval data:** SQuAD 2.0 |
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**Infrastructure**: 1x NVIDIA 3070 |
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## Model Usage |
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### Using Transformers |
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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. |
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```python |
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import torch |
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from transformers import( |
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AutoModelForQuestionAnswering, |
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AutoTokenizer, |
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pipeline |
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) |
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model_name = "sjrhuschlee/deberta-v3-large-squad2" |
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# a) Using pipelines |
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nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) |
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qa_input = { |
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'question': 'Where do I live?', |
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'context': 'My name is Sarah and I live in London' |
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} |
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res = nlp(qa_input) |
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# {'score': 0.984, 'start': 30, 'end': 37, 'answer': ' London'} |
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# b) Load model & tokenizer |
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model = AutoModelForQuestionAnswering.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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question = 'Where do I live?' |
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context = 'My name is Sarah and I live in London' |
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encoding = tokenizer(question, context, return_tensors="pt") |
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start_scores, end_scores = model( |
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encoding["input_ids"], |
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attention_mask=encoding["attention_mask"], |
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return_dict=False |
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) |
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all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0].tolist()) |
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answer_tokens = all_tokens[torch.argmax(start_scores):torch.argmax(end_scores) + 1] |
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answer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens)) |
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# 'London' |
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``` |
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## Metrics |
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```bash |
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# Squad v2 |
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{ |
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"eval_HasAns_exact": 84.83468286099865, |
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"eval_HasAns_f1": 90.48374860633226, |
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"eval_HasAns_total": 5928, |
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"eval_NoAns_exact": 91.0681244743482, |
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"eval_NoAns_f1": 91.0681244743482, |
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"eval_NoAns_total": 5945, |
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"eval_best_exact": 87.95586625115808, |
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"eval_best_exact_thresh": 0.0, |
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"eval_best_f1": 90.77635490089573, |
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"eval_best_f1_thresh": 0.0, |
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"eval_exact": 87.95586625115808, |
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"eval_f1": 90.77635490089592, |
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"eval_runtime": 623.1333, |
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"eval_samples": 11951, |
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"eval_samples_per_second": 19.179, |
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"eval_steps_per_second": 0.799, |
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"eval_total": 11873 |
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} |
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# Squad |
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{ |
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"eval_exact_match": 89.29044465468307, |
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"eval_f1": 94.9846365606959, |
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"eval_runtime": 553.7132, |
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"eval_samples": 10618, |
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"eval_samples_per_second": 19.176, |
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"eval_steps_per_second": 0.8 |
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} |
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``` |
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### Using with Peft |
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**NOTE**: This requires code in the PR https://github.com/huggingface/peft/pull/473 for the PEFT library. |
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```python |
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#!pip install peft |
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from peft import LoraConfig, PeftModelForQuestionAnswering |
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from transformers import AutoModelForQuestionAnswering, AutoTokenizer |
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model_name = "sjrhuschlee/deberta-v3-large-squad2" |
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``` |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 24 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 1 |
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- total_train_batch_size: 24 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 4.0 |
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### LoRA Config |
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``` |
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{ |
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"base_model_name_or_path": "microsoft/deberta-v3-large", |
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"bias": "none", |
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"fan_in_fan_out": false, |
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"inference_mode": true, |
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"init_lora_weights": true, |
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"lora_alpha": 32, |
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"lora_dropout": 0.1, |
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"modules_to_save": ["qa_outputs"], |
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"peft_type": "LORA", |
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"r": 8, |
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"target_modules": [ |
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"query_proj", |
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"key_proj", |
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"value_proj", |
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"dense" |
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], |
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"task_type": "QUESTION_ANS" |
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} |
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``` |
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### Framework versions |
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- Transformers 4.30.0.dev0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
