| | --- |
| | 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:** branden.chan@deepset.ai |
| | **Timo M枚ller:** timo.moeller@deepset.ai |
| | **Malte Pietsch:** malte.pietsch@deepset.ai |
| | **Tanay Soni:** tanay.soni@deepset.ai |
| | **Michel Bartels:** michel.bartels@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) |
