| --- |
| language: en |
| license: cc-by-4.0 |
| tags: |
| - deberta |
| - deberta-v3 |
| datasets: |
| - squad_v2 |
| base_model: microsoft/deberta-v3-base |
| model-index: |
| - name: deepset/deberta-v3-base-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: 83.8248 |
| name: Exact Match |
| verified: true |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiY2IyZTEyYzNlOTAwZmFlNWRiZTdiNzQzMTUyM2FmZTQ3ZWQwNWZmMzc2ZDVhYWYyMzkxOTUyMGNlMWY0M2E5MiIsInZlcnNpb24iOjF9.y8KvfefMLI977BYun0X1rAq5qudmezW_UJe9mh6sYBoiWaBosDO5TRnEGR1BHzdxmv2EgPK_PSomtZvb043jBQ |
| - type: f1 |
| value: 87.41 |
| name: F1 |
| verified: true |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOWVhNjAwM2Q5N2Y3MGU4ZWY3N2Y0MmNjYWYwYmQzNTdiYWExODhkYmQ1YjIwM2I1ODEzNWIxZDI1ZWQ1YWRjNSIsInZlcnNpb24iOjF9.Jk0v1ZheLRFz6k9iNAgCMMZtPYj5eVwUCku4E76wRYc-jHPmiUuxvNiNkn6NW-jkBD8bJGMqDSjJyVpVMn9pBA |
| - task: |
| type: question-answering |
| name: Question Answering |
| dataset: |
| name: squad |
| type: squad |
| config: plain_text |
| split: validation |
| metrics: |
| - type: exact_match |
| value: 84.9678 |
| name: Exact Match |
| verified: true |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOWUxYTg4MzU3YTdmMDRmMGM0NjFjMTcwNGM3YzljM2RkMTc1ZGNhMDQwMTgwNGI0ZDE4ZGMxZTE3YjY5YzQ0ZiIsInZlcnNpb24iOjF9.KKaJ1UtikNe2g6T8XhLoWNtL9X4dHHyl_O4VZ5LreBT9nXneGc21lI1AW3n8KXTFGemzRpRMvmCDyKVDHucdDQ |
| - type: f1 |
| value: 92.2777 |
| name: F1 |
| verified: true |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDU0ZTQwMzg4ZDY1ZWYxOGIxMzY2ODljZTBkMTNlYjA0ODBjNjcxNTg3ZDliYWU1YTdkYTM2NTIxOTg1MGM4OCIsInZlcnNpb24iOjF9.8VHg1BXx6gLw_K7MUK2QSE80Y9guiVR8n8K8nX4laGsLibxv5u_yDv9F3ahbUa1eZG_bbidl93TY2qFUiYHtAQ |
| - task: |
| type: question-answering |
| name: Question Answering |
| dataset: |
| name: adversarial_qa |
| type: adversarial_qa |
| config: adversarialQA |
| split: validation |
| metrics: |
| - type: exact_match |
| value: 30.733 |
| name: Exact Match |
| - type: f1 |
| value: 44.099 |
| 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: 79.295 |
| name: Exact Match |
| - type: f1 |
| value: 86.609 |
| name: F1 |
| - task: |
| type: question-answering |
| name: Question Answering |
| dataset: |
| name: squadshifts amazon |
| type: squadshifts |
| config: amazon |
| split: test |
| metrics: |
| - type: exact_match |
| value: 68.680 |
| name: Exact Match |
| - type: f1 |
| value: 83.832 |
| 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: 80.171 |
| name: Exact Match |
| - type: f1 |
| value: 90.452 |
| name: F1 |
| - task: |
| type: question-answering |
| name: Question Answering |
| dataset: |
| name: squadshifts nyt |
| type: squadshifts |
| config: nyt |
| split: test |
| metrics: |
| - type: exact_match |
| value: 81.570 |
| name: Exact Match |
| - type: f1 |
| value: 90.644 |
| name: F1 |
| - task: |
| type: question-answering |
| name: Question Answering |
| dataset: |
| name: squadshifts reddit |
| type: squadshifts |
| config: reddit |
| split: test |
| metrics: |
| - type: exact_match |
| value: 66.990 |
| name: Exact Match |
| - type: f1 |
| value: 80.231 |
| name: F1 |
| --- |
| |
| # deberta-v3-base for Extractive QA |
|
|
| This is the [deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-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:** deberta-v3-base |
| **Language:** English |
| **Downstream-task:** Extractive QA |
| **Training data:** SQuAD 2.0 |
| **Eval data:** SQuAD 2.0 |
| **Code:** See [an example extractive QA pipeline built with Haystack](https://haystack.deepset.ai/tutorials/34_extractive_qa_pipeline) |
| **Infrastructure**: 1x NVIDIA A10G |
|
|
| ## Hyperparameters |
|
|
| ``` |
| batch_size = 12 |
| n_epochs = 4 |
| base_LM_model = "deberta-v3-base" |
| max_seq_len = 512 |
| learning_rate = 2e-5 |
| lr_schedule = LinearWarmup |
| warmup_proportion = 0.2 |
| doc_stride = 128 |
| max_query_length = 64 |
| ``` |
|
|
| ## Usage |
|
|
| ### In Haystack |
| Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. |
| To load and run the model with [Haystack](https://github.com/deepset-ai/haystack/): |
| ```python |
| # After running pip install haystack-ai "transformers[torch,sentencepiece]" |
| |
| from haystack import Document |
| from haystack.components.readers import ExtractiveReader |
| |
| docs = [ |
| Document(content="Python is a popular programming language"), |
| Document(content="python ist eine beliebte Programmiersprache"), |
| ] |
| |
| reader = ExtractiveReader(model="deepset/roberta-base-squad2") |
| reader.warm_up() |
| |
| question = "What is a popular programming language?" |
| result = reader.run(query=question, documents=docs) |
| # {'answers': [ExtractedAnswer(query='What is a popular programming language?', score=0.5740374326705933, data='python', document=Document(id=..., content: '...'), context=None, document_offset=ExtractedAnswer.Span(start=0, end=6),...)]} |
| ``` |
| For a complete example with an extractive question answering pipeline that scales over many documents, check out the [corresponding Haystack tutorial](https://haystack.deepset.ai/tutorials/34_extractive_qa_pipeline). |
|
|
| ### 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) |
| ``` |
|
|
|
|
| ## Authors |
| **Sebastian Lee:** sebastian.lee [at] deepset.ai |
| **Timo M枚ller:** timo.moeller [at] deepset.ai |
| **Malte Pietsch:** malte.pietsch [at] 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 production-ready open-source AI framework [Haystack](https://haystack.deepset.ai/). |
|
|
| Some of our other work: |
| - [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")](https://huggingface.co/deepset/tinyroberta-squad2) |
| - [German BERT](https://deepset.ai/german-bert), [GermanQuAD and GermanDPR](https://deepset.ai/germanquad), [German embedding model](https://huggingface.co/mixedbread-ai/deepset-mxbai-embed-de-large-v1) |
| - [deepset Cloud](https://www.deepset.ai/deepset-cloud-product), [deepset Studio](https://www.deepset.ai/deepset-studio) |
|
|
| ## 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/Haystack_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://haystack.deepset.ai/) | [YouTube](https://www.youtube.com/@deepset_ai) |
|
|
| By the way: [we're hiring!](http://www.deepset.ai/jobs) |
