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--- |
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language: en |
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datasets: |
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- squad_v2 |
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license: cc-by-4.0 |
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model-index: |
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- name: autoevaluate/roberta-base-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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- name: Exact Match |
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type: exact_match |
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value: 79.9309 |
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verified: true |
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- name: F1 |
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type: f1 |
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value: 82.9501 |
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verified: true |
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- name: total |
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type: total |
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value: 11869 |
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verified: true |
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--- |
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# roberta-base for QA |
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> Note: this is a clone of [`roberta-base-squad2`](https://huggingface.co/deepset/roberta-base-squad2) for internal testing. |
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This is the [roberta-base](https://huggingface.co/roberta-base) 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:** roberta-base |
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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**: 4x Tesla v100 |
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## Hyperparameters |
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``` |
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batch_size = 96 |
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n_epochs = 2 |
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base_LM_model = "roberta-base" |
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max_seq_len = 386 |
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learning_rate = 3e-5 |
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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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## Using a distilled model instead |
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Please note that we have also released a distilled version of this model called [deepset/tinyroberta-squad2](https://huggingface.co/deepset/tinyroberta-squad2). The distilled model has a comparable prediction quality and runs at twice the speed of the base model. |
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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/roberta-base-squad2") |
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# or |
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reader = TransformersReader(model_name_or_path="deepset/roberta-base-squad2",tokenizer="deepset/roberta-base-squad2") |
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``` |
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For a complete example of ``roberta-base-squad2`` being used for Question Answering, check out the [Tutorials in Haystack Documentation](https://haystack.deepset.ai/tutorials/first-qa-system) |
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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/roberta-base-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": 79.87029394424324, |
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"f1": 82.91251169582613, |
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"total": 11873, |
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"HasAns_exact": 77.93522267206478, |
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"HasAns_f1": 84.02838248389763, |
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"HasAns_total": 5928, |
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"NoAns_exact": 81.79983179142137, |
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"NoAns_f1": 81.79983179142137, |
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"NoAns_total": 5945 |
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``` |
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Using the official [question answering notebook](https://github.com/huggingface/notebooks/blob/main/examples/question_answering.ipynb) from `transformers` yields: |
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``` |
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{'HasAns_exact': 77.93522267206478, |
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'HasAns_f1': 83.93715663402219, |
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'HasAns_total': 5928, |
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'NoAns_exact': 81.90075693860386, |
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'NoAns_f1': 81.90075693860386, |
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'NoAns_total': 5945, |
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'best_exact': 79.92082877116145, |
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'best_exact_thresh': 0.0, |
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'best_f1': 82.91749890730902, |
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'best_f1_thresh': 0.0, |
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'exact': 79.92082877116145, |
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'f1': 82.91749890730917, |
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'total': 11873} |
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``` |
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which is consistent with the officially reported results. Using the question answering `Evaluator` from `evaluate` gives: |
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``` |
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{'HasAns_exact': 77.91835357624831, |
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'HasAns_f1': 84.07820736158186, |
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'HasAns_total': 5928, |
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'NoAns_exact': 81.91757779646763, |
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'NoAns_f1': 81.91757779646763, |
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'NoAns_total': 5945, |
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'best_exact': 79.92082877116145, |
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'best_exact_thresh': 0.996823787689209, |
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'best_f1': 82.99634576260925, |
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'best_f1_thresh': 0.996823787689209, |
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'exact': 79.92082877116145, |
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'f1': 82.9963457626089, |
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'latency_in_seconds': 0.016523243643392558, |
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'samples_per_second': 60.52080460605492, |
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'total': 11873, |
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'total_time_in_seconds': 196.18047177799986} |
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``` |
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which is also consistent with the officially reported results. |
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## Authors |
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**Branden Chan:** branden.chan@deepset.ai |
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**Timo M枚ller:** timo.moeller@deepset.ai |
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**Malte Pietsch:** malte.pietsch@deepset.ai |
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**Tanay Soni:** tanay.soni@deepset.ai |
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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"><img alt="slack" class="h-7 inline-block m-0" style="margin: 0" src="https://huggingface.co/spaces/deepset/README/resolve/main/Slack_RGB.png"/>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/) | [Slack](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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