jaimin
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Bullet_Point

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----
-language: en
-datasets:
-- squad_v2
-license: cc-by-4.0
-model-index:
-- name: deepset/roberta-base-squad2
-  results:
-  - task:
-      type: question-answering
-      name: Question Answering
-    dataset:
-      name: squad_v2
-      type: squad_v2
-      config: squad_v2
-      split: validation
-    metrics:
-    - name: Exact Match
-      type: exact_match
-      value: 79.9309
-      verified: true
-    - name: F1
-      type: f1
-      value: 82.9501
-      verified: true
-    - name: total
-      type: total
-      value: 11869
-      verified: true
----
-# roberta-base for QA
-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.
-## Overview
-**Language model:** roberta-base
-**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
-```
-batch_size = 96
-n_epochs = 2
-base_LM_model = "roberta-base"
-max_seq_len = 386
-learning_rate = 3e-5
-lr_schedule = LinearWarmup
-warmup_proportion = 0.2
-doc_stride=128
-max_query_length=64
-```
-## Using a distilled model instead
-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.
-## 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-base-squad2")
-# or
-reader = TransformersReader(model_name_or_path="deepset/roberta-base-squad2",tokenizer="deepset/roberta-base-squad2")
-```
-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)
-### In Transformers
-```python
-from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
-model_name = "deepset/roberta-base-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)
-```
-## Performance
-Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/).
-```
-"exact": 79.87029394424324,
-"f1": 82.91251169582613,
-"total": 11873,
-"HasAns_exact": 77.93522267206478,
-"HasAns_f1": 84.02838248389763,
-"HasAns_total": 5928,
-"NoAns_exact": 81.79983179142137,
-"NoAns_f1": 81.79983179142137,
-"NoAns_total": 5945
-```
-## 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://haystack.deepset.ai">Documentation</a></strong>.
-We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community/join">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/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-By the way: [we're hiring!](http://www.deepset.ai/jobs)


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