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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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- mrqa |
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- synQA |
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- adversarial_qa |
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datasets: |
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- squad_v2 |
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- squad |
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- mrqa |
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- mbartolo/synQA |
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- UCLNLP/adversarial_qa |
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- newsqa |
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- trivia_qa |
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- search_qa |
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- hotpot_qa |
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- natural_questions |
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pipeline_tag: question-answering |
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base_model: microsoft/deberta-v3-base |
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model-index: |
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- name: sjrhuschlee/deberta-v3-base-squad2-ext-v1 |
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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: 79.483 |
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name: Exact Match |
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- type: f1 |
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value: 82.343 |
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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: 87.985 |
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name: Exact Match |
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- type: f1 |
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value: 93.651 |
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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: 47.533 |
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name: Exact Match |
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- type: f1 |
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value: 59.838 |
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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: 84.723 |
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name: Exact Match |
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- type: f1 |
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value: 89.78 |
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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: 74.851 |
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name: Exact Match |
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- type: f1 |
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value: 87.448 |
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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: 83.396 |
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name: Exact Match |
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- type: f1 |
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value: 91.996 |
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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: 83.934 |
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name: Exact Match |
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- type: f1 |
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value: 92.234 |
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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: 75.008 |
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name: Exact Match |
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- type: f1 |
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value: 86.12 |
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name: F1 |
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--- |
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# deberta-v3-base for Extractive QA |
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This is the [deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) model, fine-tuned using the SQuAD 2.0, MRQA, AdversarialQA, and SynQA datasets. It's been trained on question-answer pairs, including unanswerable questions, for the task of Extractive Question Answering. |
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## Overview |
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**Language model:** deberta-v3-base |
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**Language:** English |
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**Downstream-task:** Extractive QA |
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**Training data:** SQuAD 2.0, MRQA, AdversarialQA, SynQA |
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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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```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-base-squad2-ext-v1" |
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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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## Dataset Preparation |
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The MRQA dataset was updated to fix some errors and formatting to work with the `run_qa.py` example script provided in the Hugging Face Transformers library. |
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The changes included |
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- Updating incorrect answer starts locations (usually off by a few characters) |
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- Updating the answer text to exactly match the text found in the context |
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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: 1e-06 |
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- train_batch_size: 12 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 96 |
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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: 3.0 |
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### Framework versions |
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- Transformers 4.31.0.dev0 |
