draft update for the model card
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README.md
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README.md
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@ -7,11 +7,8 @@ license: cc-by-4.0
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# roberta-base for QA
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NOTE: This is version 2 of the model. See [this github issue](https://github.com/deepset-ai/FARM/issues/552) from the FARM repository for an explanation of why we updated. If you'd like to use version 1, specify `revision="v1.0"` when loading the model in Transformers 3.5. For exmaple:
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```
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model_name = "deepset/roberta-base-squad2"
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pipeline(model=model_name, tokenizer=model_name, revision="v1.0", task="question-answering")
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```
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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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@ -19,7 +16,7 @@ pipeline(model=model_name, tokenizer=model_name, revision="v1.0", task="question
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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 [example](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering.py) in [FARM](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering.py)
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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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@ -39,24 +36,17 @@ max_query_length=64
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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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## 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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## 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 Hasytack 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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@ -76,53 +66,51 @@ model = AutoModelForQuestionAnswering.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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```
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### In FARM
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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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```python
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from farm.modeling.adaptive_model import AdaptiveModel
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from farm.modeling.tokenization import Tokenizer
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from farm.infer import Inferencer
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model_name = "deepset/roberta-base-squad2"
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# a) Get predictions
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nlp = Inferencer.load(model_name, task_type="question_answering")
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QA_input = [{"questions": ["Why is model conversion important?"],
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"text": "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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res = nlp.inference_from_dicts(dicts=QA_input, rest_api_schema=True)
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# b) Load model & tokenizer
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model = AdaptiveModel.convert_from_transformers(model_name, device="cpu", task_type="question_answering")
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tokenizer = Tokenizer.load(model_name)
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```
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"exact": 79.87029394424324,
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"f1": 82.91251169582613,
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### In haystack
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For doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also 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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"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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## Authors
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Branden Chan: `branden.chan [at] deepset.ai`
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Timo Möller: `timo.moeller [at] deepset.ai`
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Malte Pietsch: `malte.pietsch [at] deepset.ai`
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Tanay Soni: `tanay.soni [at] deepset.ai`
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**Branden Chan:** branden.chan@deepset.ai
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**Timo Möller:** timo.moelle@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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We bring NLP to the industry via open source!
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Our focus: Industry specific language models & large scale QA systems.
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Some of our work:
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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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- [FARM](https://github.com/deepset-ai/FARM)
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- [Haystack](https://github.com/deepset-ai/haystack/)
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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>. You can also <strong><a class="h-7" href="https://haystack.deepset.ai/community/join">join us on <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"/></a></strong></p>
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Get in touch:
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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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