| Quantization made by Richard Erkhov. | |
| [Github](https://github.com/RichardErkhov) | |
| [Discord](https://discord.gg/pvy7H8DZMG) | |
| [Request more models](https://github.com/RichardErkhov/quant_request) | |
| roberta-large-squad2 - bnb 8bits | |
| - Model creator: https://huggingface.co/deepset/ | |
| - Original model: https://huggingface.co/deepset/roberta-large-squad2/ | |
| Original model description: | |
| --- | |
| language: en | |
| license: cc-by-4.0 | |
| datasets: | |
| - squad_v2 | |
| base_model: roberta-large | |
| model-index: | |
| - name: deepset/roberta-large-squad2 | |
| results: | |
| - task: | |
| type: question-answering | |
| name: Question Answering | |
| dataset: | |
| name: squad_v2 | |
| type: squad_v2 | |
| config: squad_v2 | |
| split: validation | |
| metrics: | |
| - type: exact_match | |
| value: 85.168 | |
| name: Exact Match | |
| - type: f1 | |
| value: 88.349 | |
| name: F1 | |
| - task: | |
| type: question-answering | |
| name: Question Answering | |
| dataset: | |
| name: squad | |
| type: squad | |
| config: plain_text | |
| split: validation | |
| metrics: | |
| - type: exact_match | |
| value: 87.162 | |
| name: Exact Match | |
| - type: f1 | |
| value: 93.603 | |
| name: F1 | |
| - task: | |
| type: question-answering | |
| name: Question Answering | |
| dataset: | |
| name: adversarial_qa | |
| type: adversarial_qa | |
| config: adversarialQA | |
| split: validation | |
| metrics: | |
| - type: exact_match | |
| value: 35.900 | |
| name: Exact Match | |
| - type: f1 | |
| value: 48.923 | |
| name: F1 | |
| - task: | |
| type: question-answering | |
| name: Question Answering | |
| dataset: | |
| name: squad_adversarial | |
| type: squad_adversarial | |
| config: AddOneSent | |
| split: validation | |
| metrics: | |
| - type: exact_match | |
| value: 81.142 | |
| name: Exact Match | |
| - type: f1 | |
| value: 87.099 | |
| name: F1 | |
| - task: | |
| type: question-answering | |
| name: Question Answering | |
| dataset: | |
| name: squadshifts amazon | |
| type: squadshifts | |
| config: amazon | |
| split: test | |
| metrics: | |
| - type: exact_match | |
| value: 72.453 | |
| name: Exact Match | |
| - type: f1 | |
| value: 86.325 | |
| name: F1 | |
| - task: | |
| type: question-answering | |
| name: Question Answering | |
| dataset: | |
| name: squadshifts new_wiki | |
| type: squadshifts | |
| config: new_wiki | |
| split: test | |
| metrics: | |
| - type: exact_match | |
| value: 82.338 | |
| name: Exact Match | |
| - type: f1 | |
| value: 91.974 | |
| name: F1 | |
| - task: | |
| type: question-answering | |
| name: Question Answering | |
| dataset: | |
| name: squadshifts nyt | |
| type: squadshifts | |
| config: nyt | |
| split: test | |
| metrics: | |
| - type: exact_match | |
| value: 84.352 | |
| name: Exact Match | |
| - type: f1 | |
| value: 92.645 | |
| name: F1 | |
| - task: | |
| type: question-answering | |
| name: Question Answering | |
| dataset: | |
| name: squadshifts reddit | |
| type: squadshifts | |
| config: reddit | |
| split: test | |
| metrics: | |
| - type: exact_match | |
| value: 74.722 | |
| name: Exact Match | |
| - type: f1 | |
| value: 86.860 | |
| name: F1 | |
| --- | |
| # roberta-large for QA | |
| This is the [roberta-large](https://huggingface.co/roberta-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. | |
| ## Overview | |
| **Language model:** roberta-large | |
| **Language:** English | |
| **Downstream-task:** Extractive QA | |
| **Training data:** SQuAD 2.0 | |
| **Eval data:** SQuAD 2.0 | |
| **Code:** See [an example QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/first-qa-system) | |
| **Infrastructure**: 4x Tesla v100 | |
| ## Hyperparameters | |
| ``` | |
| base_LM_model = "roberta-large" | |
| ``` | |
| ## Using a distilled model instead | |
| Please note that we have also released a distilled version of this model called [deepset/roberta-base-squad2-distilled](https://huggingface.co/deepset/roberta-base-squad2-distilled). The distilled model has a comparable prediction quality and runs at twice the speed of the large model. | |
| ## Usage | |
| ### In Haystack | |
| 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/): | |
| ```python | |
| reader = FARMReader(model_name_or_path="deepset/roberta-large-squad2") | |
| # or | |
| reader = TransformersReader(model_name_or_path="deepset/roberta-large-squad2",tokenizer="deepset/roberta-large-squad2") | |
| ``` | |
| For a complete example of ``roberta-large-squad2`` being used for Question Answering, check out the [Tutorials in Haystack Documentation](https://haystack.deepset.ai/tutorials/first-qa-system) | |
| ### In Transformers | |
| ```python | |
| from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline | |
| model_name = "deepset/roberta-large-squad2" | |
| # a) Get predictions | |
| nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) | |
| QA_input = { | |
| 'question': 'Why is model conversion important?', | |
| 'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.' | |
| } | |
| res = nlp(QA_input) | |
| # b) Load model & tokenizer | |
| model = AutoModelForQuestionAnswering.from_pretrained(model_name) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| ``` | |
| ## Authors | |
| **Branden Chan:** branden.chan@deepset.ai | |
| **Timo Möller:** timo.moeller@deepset.ai | |
| **Malte Pietsch:** malte.pietsch@deepset.ai | |
| **Tanay Soni:** tanay.soni@deepset.ai | |
| ## About us | |
| <div class="grid lg:grid-cols-2 gap-x-4 gap-y-3"> | |
| <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> | |
| <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/deepset-logo-colored.png" class="w-40"/> | |
| </div> | |
| <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> | |
| <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/haystack-logo-colored.png" class="w-40"/> | |
| </div> | |
| </div> | |
| [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. | |
| Some of our other work: | |
| - [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")]([https://huggingface.co/deepset/tinyroberta-squad2) | |
| - [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert) | |
| - [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad) | |
| ## Get in touch and join the Haystack community | |
| <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://docs.haystack.deepset.ai">Documentation</a></strong>. | |
| We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community">Discord community open to everyone!</a></strong></p> | |
| [Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai) | |
| By the way: [we're hiring!](http://www.deepset.ai/jobs) | |