| --- |
| language: en |
| license: cc-by-4.0 |
| datasets: |
| - squad_v2 |
| model-index: |
| - name: deepset/tinyroberta-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: 78.8627 |
| name: Exact Match |
| verified: true |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDNlZDU4ODAxMzY5NGFiMTMyZmQ1M2ZhZjMyODA1NmFlOGMxNzYxNTA4OGE5YTBkZWViZjBkNGQ2ZmMxZjVlMCIsInZlcnNpb24iOjF9.Wgu599r6TvgMLTrHlLMVAbUtKD_3b70iJ5QSeDQ-bRfUsVk6Sz9OsJCp47riHJVlmSYzcDj_z_3jTcUjCFFXBg |
| - type: f1 |
| value: 82.0355 |
| name: F1 |
| verified: true |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTFkMzEzMWNiZDRhMGZlODhkYzcwZTZiMDFjZDg2YjllZmUzYWM5NTgwNGQ2NGYyMDk2ZGQwN2JmMTE5NTc3YiIsInZlcnNpb24iOjF9.ChgaYpuRHd5WeDFjtiAHUyczxtoOD_M5WR8834jtbf7wXhdGOnZKdZ1KclmhoI5NuAGc1NptX-G0zQ5FTHEcBA |
| - task: |
| type: question-answering |
| name: Question Answering |
| dataset: |
| name: squad |
| type: squad |
| config: plain_text |
| split: validation |
| metrics: |
| - type: exact_match |
| value: 83.860 |
| name: Exact Match |
| - type: f1 |
| value: 90.752 |
| 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: 25.967 |
| name: Exact Match |
| - type: f1 |
| value: 37.006 |
| 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: 76.329 |
| name: Exact Match |
| - type: f1 |
| value: 83.292 |
| name: F1 |
| - task: |
| type: question-answering |
| name: Question Answering |
| dataset: |
| name: squadshifts amazon |
| type: squadshifts |
| config: amazon |
| split: test |
| metrics: |
| - type: exact_match |
| value: 63.915 |
| name: Exact Match |
| - type: f1 |
| value: 78.395 |
| 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: 80.297 |
| name: Exact Match |
| - type: f1 |
| value: 89.808 |
| name: F1 |
| - task: |
| type: question-answering |
| name: Question Answering |
| dataset: |
| name: squadshifts nyt |
| type: squadshifts |
| config: nyt |
| split: test |
| metrics: |
| - type: exact_match |
| value: 80.149 |
| name: Exact Match |
| - type: f1 |
| value: 88.321 |
| name: F1 |
| - task: |
| type: question-answering |
| name: Question Answering |
| dataset: |
| name: squadshifts reddit |
| type: squadshifts |
| config: reddit |
| split: test |
| metrics: |
| - type: exact_match |
| value: 66.959 |
| name: Exact Match |
| - type: f1 |
| value: 79.300 |
| name: F1 |
| --- |
| |
| # tinyroberta for Extractive QA |
|
|
| This is the *distilled* version of the [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) model. This model has a comparable prediction quality and runs at twice the speed of the base model. |
|
|
| ## Overview |
| **Language model:** tinyroberta-squad2 |
| **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**: 4x Tesla v100 |
|
|
| ## Hyperparameters |
|
|
| ``` |
| batch_size = 96 |
| n_epochs = 4 |
| base_LM_model = "deepset/tinyroberta-squad2-step1" |
| max_seq_len = 384 |
| learning_rate = 3e-5 |
| lr_schedule = LinearWarmup |
| warmup_proportion = 0.2 |
| doc_stride = 128 |
| max_query_length = 64 |
| distillation_loss_weight = 0.75 |
| temperature = 1.5 |
| teacher = "deepset/robert-large-squad2" |
| ``` |
|
|
| ## Distillation |
| This model was distilled using the TinyBERT approach described in [this paper](https://arxiv.org/pdf/1909.10351.pdf) and implemented in [haystack](https://github.com/deepset-ai/haystack). |
| Firstly, we have performed intermediate layer distillation with roberta-base as the teacher which resulted in [deepset/tinyroberta-6l-768d](https://huggingface.co/deepset/tinyroberta-6l-768d). |
| Secondly, we have performed task-specific distillation with [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) as the teacher for further intermediate layer distillation on an augmented version of SQuADv2 and then with [deepset/roberta-large-squad2](https://huggingface.co/deepset/roberta-large-squad2) as the teacher for prediction layer distillation. |
|
|
| ## 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/tinyroberta-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/tinyroberta-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": 78.69114798281817, |
| "f1": 81.9198998536977, |
| |
| "total": 11873, |
| "HasAns_exact": 76.19770580296895, |
| "HasAns_f1": 82.66446878592329, |
| "HasAns_total": 5928, |
| "NoAns_exact": 81.17746005046257, |
| "NoAns_f1": 81.17746005046257, |
| "NoAns_total": 5945 |
| ``` |
|
|
| ## Authors |
| **Branden Chan:** [email protected] |
| **Timo M枚ller:** [email protected] |
| **Malte Pietsch:** [email protected] |
| **Tanay Soni:** [email protected] |
| **Michel Bartels:** [email protected] |
|
|
| ## 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) |
