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--- |
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language: en |
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license: cc-by-4.0 |
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tags: |
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- deberta |
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- deberta-v3 |
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- deberta-v3-large |
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datasets: |
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- squad_v2 |
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model-index: |
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- name: deepset/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: 88.0876 |
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name: Exact Match |
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verified: true |
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verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZmE0MWEwNjBkNTA1MmU0ZDkyYTA1OGEwNzY3NGE4NWU4NGI0NTQzNjRlNjY1NGRmNDU2MjA0NjU1N2JlZmNhYiIsInZlcnNpb24iOjF9.PnBF_vD0HujNBSShGJzsJnjmiBP_qT8xb2E7ORmpKfNspKXEuN_pBk9iV0IHRzdqOSyllcxlCv93XMPblNjWDw |
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- type: f1 |
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value: 91.1623 |
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name: F1 |
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verified: true |
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verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDBkNDUzZmNkNDQwOGRkMmVlZjkxZWVlMzk3NzFmMGIxMTFmMjZlZDcyOWFiMjljNjM5MThlZDM4OWRmNzMwOCIsInZlcnNpb24iOjF9.bacyetziNI2DxO67GWpTyeRPXqF1POkyv00wEHXlyZu71pZngsNpZyrnuj2aJlCqQwHGnF_lT2ysaXKHprQRBg |
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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: 89.2366 |
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name: Exact Match |
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verified: true |
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verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjQ1Yjk3YTdiYTY1NmYxMTI1ZGZlMjRkNTlhZTkyNjRkNjgxYWJiNDk2NzE3NjAyYmY3YmRjNjg4YmEyNDkyYyIsInZlcnNpb24iOjF9.SEWyqX_FPQJOJt2KjOCNgQ2giyVeLj5bmLI5LT_Pfo33tbWPWD09TySYdsthaVTjUGT5DvDzQLASSwBH05FyBw |
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- type: f1 |
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value: 95.0569 |
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name: F1 |
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verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiY2QyODQ1NWVlYjQxMjA0YTgyNmQ2NmIxOWY3MDRmZjE3ZWI5Yjc4ZDE4NzA2YjE2YTE1YTBlNzNiYmNmNzI3NCIsInZlcnNpb24iOjF9.NcXEc9xoggV76w1bQKxuJDYbOTxFzdny2k-85_b6AIMtfpYV3rGR1Z5YF6tVY2jyp7mgm5Jd5YSgGI3NvNE-CQ |
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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: 42.100 |
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name: Exact Match |
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- type: f1 |
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value: 56.587 |
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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: 83.548 |
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name: Exact Match |
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- type: f1 |
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value: 89.385 |
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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: 72.979 |
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name: Exact Match |
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- type: f1 |
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value: 87.254 |
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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: 83.938 |
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name: Exact Match |
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- type: f1 |
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value: 92.695 |
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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: 85.534 |
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name: Exact Match |
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- type: f1 |
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value: 93.153 |
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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: 73.284 |
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name: Exact Match |
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- type: f1 |
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value: 85.307 |
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name: F1 |
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--- |
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# deberta-v3-large for 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 Question Answering. |
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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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**Code:** See [an example QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/first-qa-system) |
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**Infrastructure**: 1x NVIDIA A10G |
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## Hyperparameters |
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``` |
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batch_size = 2 |
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grad_acc_steps = 32 |
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n_epochs = 6 |
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base_LM_model = "microsoft/deberta-v3-large" |
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max_seq_len = 512 |
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learning_rate = 7e-6 |
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lr_schedule = LinearWarmup |
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warmup_proportion = 0.2 |
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doc_stride=128 |
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max_query_length=64 |
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``` |
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## Usage |
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### In Haystack |
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Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in [Haystack](https://github.com/deepset-ai/haystack/): |
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```python |
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reader = FARMReader(model_name_or_path="deepset/deberta-v3-large-squad2") |
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# or |
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reader = TransformersReader(model_name_or_path="deepset/deberta-v3-large-squad2",tokenizer="deepset/deberta-v3-large-squad2") |
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``` |
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### In Transformers |
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```python |
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from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline |
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model_name = "deepset/deberta-v3-large-squad2" |
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# a) Get predictions |
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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': 'Why is model conversion important?', |
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'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.' |
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} |
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res = nlp(QA_input) |
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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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``` |
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## Performance |
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Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/). |
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``` |
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"exact": 87.6105449338836, |
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"f1": 90.75307008866517, |
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"total": 11873, |
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"HasAns_exact": 84.37921727395411, |
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"HasAns_f1": 90.6732795483674, |
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"HasAns_total": 5928, |
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"NoAns_exact": 90.83263246425568, |
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"NoAns_f1": 90.83263246425568, |
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"NoAns_total": 5945 |
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``` |
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## About us |
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<div class="grid lg:grid-cols-2 gap-x-4 gap-y-3"> |
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<div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> |
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<img alt="" src="https://huggingface.co/spaces/deepset/README/resolve/main/haystack-logo-colored.svg" class="w-40"/> |
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</div> |
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<div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> |
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<img alt="" src="https://huggingface.co/spaces/deepset/README/resolve/main/deepset-logo-colored.svg" class="w-40"/> |
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</div> |
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</div> |
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[deepset](http://deepset.ai/) is the company behind the open-source NLP framework [Haystack](https://haystack.deepset.ai/) which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc. |
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Some of our other work: |
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- [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")]([https://huggingface.co/deepset/tinyroberta-squad2) |
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- [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert) |
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- [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad) |
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## Get in touch and join the Haystack community |
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<p>For more info on Haystack, visit our <strong><a href="https://github.com/deepset-ai/haystack">GitHub</a></strong> repo and <strong><a href="https://haystack.deepset.ai">Documentation</a></strong>. |
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We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community/join">Discord community open to everyone!</a></strong></p> |
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[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai) |
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By the way: [we're hiring!](http://www.deepset.ai/jobs) |
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