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--- |
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language: en |
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license: mit |
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tags: |
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- exbert |
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datasets: |
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- squad_v2 |
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thumbnail: https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg |
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model-index: |
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- name: deepset/roberta-base-squad2-distilled |
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results: |
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- task: |
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type: question-answering |
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name: Question Answering |
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dataset: |
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name: squad_v2 |
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type: squad_v2 |
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config: squad_v2 |
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split: validation |
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metrics: |
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- type: exact_match |
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value: 80.8593 |
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name: Exact Match |
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verified: true |
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verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzVjNzkxNmNiNDkzNzdiYjJjZGM3ZTViMGJhOGM2ZjFmYjg1MjYxMDM2YzM5NWMwNDIyYzNlN2QwNGYyNDMzZSIsInZlcnNpb24iOjF9.Rgww8tf8D7nF2dh2U_DMrFzmp87k8s7RFibrDXSvQyA66PGWXwjlsd1552lzjHnNV5hvHUM1-h3PTuY_5p64BA |
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- type: f1 |
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value: 84.0104 |
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name: F1 |
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verified: true |
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verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTAyZDViNWYzNjA4OWQ5MzgyYmQ2ZDlhNWRhMTIzYTYxYzViMmI4NWE4ZGU5MzVhZTAwNTRlZmRlNWUwMjI0ZSIsInZlcnNpb24iOjF9.Er21BNgJ3jJXLuZtpubTYq9wCwO1i_VLQFwS5ET0e4eAYVVj0aOA40I5FvP5pZac3LjkCnVacxzsFWGCYVmnDA |
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- task: |
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type: question-answering |
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name: Question Answering |
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dataset: |
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name: squad |
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type: squad |
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config: plain_text |
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split: validation |
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metrics: |
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- type: exact_match |
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value: 86.225 |
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name: Exact Match |
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- type: f1 |
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value: 92.483 |
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name: F1 |
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- task: |
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type: question-answering |
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name: Question Answering |
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dataset: |
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name: adversarial_qa |
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type: adversarial_qa |
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config: adversarialQA |
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split: validation |
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metrics: |
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- type: exact_match |
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value: 29.900 |
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name: Exact Match |
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- type: f1 |
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value: 41.183 |
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name: F1 |
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- task: |
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type: question-answering |
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name: Question Answering |
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dataset: |
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name: squad_adversarial |
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type: squad_adversarial |
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config: AddOneSent |
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split: validation |
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metrics: |
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- type: exact_match |
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value: 79.071 |
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name: Exact Match |
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- type: f1 |
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value: 84.472 |
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name: F1 |
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- task: |
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type: question-answering |
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name: Question Answering |
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dataset: |
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name: squadshifts amazon |
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type: squadshifts |
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config: amazon |
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split: test |
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metrics: |
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- type: exact_match |
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value: 70.733 |
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name: Exact Match |
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- type: f1 |
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value: 83.958 |
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name: F1 |
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- task: |
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type: question-answering |
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name: Question Answering |
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dataset: |
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name: squadshifts new_wiki |
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type: squadshifts |
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config: new_wiki |
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split: test |
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metrics: |
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- type: exact_match |
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value: 82.011 |
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name: Exact Match |
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- type: f1 |
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value: 91.092 |
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name: F1 |
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- task: |
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type: question-answering |
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name: Question Answering |
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dataset: |
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name: squadshifts nyt |
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type: squadshifts |
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config: nyt |
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split: test |
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metrics: |
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- type: exact_match |
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value: 84.203 |
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name: Exact Match |
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- type: f1 |
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value: 91.521 |
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name: F1 |
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- task: |
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type: question-answering |
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name: Question Answering |
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dataset: |
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name: squadshifts reddit |
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type: squadshifts |
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config: reddit |
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split: test |
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metrics: |
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- type: exact_match |
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value: 72.029 |
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name: Exact Match |
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- type: f1 |
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value: 83.454 |
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name: F1 |
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--- |
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# roberta-base distilled for Extractive QA |
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|
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## Overview |
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**Language model:** deepset/roberta-base-squad2-distilled |
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**Language:** English |
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**Training data:** SQuAD 2.0 training set |
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**Eval data:** SQuAD 2.0 dev set |
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**Code:** See [an example extractive QA pipeline built with Haystack](https://haystack.deepset.ai/tutorials/34_extractive_qa_pipeline) |
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**Infrastructure**: 4x V100 GPU |
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**Published**: Dec 8th, 2021 |
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## Details |
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- haystack's distillation feature was used for training. deepset/roberta-large-squad2 was used as the teacher model. |
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## Hyperparameters |
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``` |
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batch_size = 80 |
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n_epochs = 4 |
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max_seq_len = 384 |
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learning_rate = 3e-5 |
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lr_schedule = LinearWarmup |
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embeds_dropout_prob = 0.1 |
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temperature = 1.5 |
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distillation_loss_weight = 0.75 |
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``` |
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## Usage |
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|
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### In Haystack |
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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. |
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To load and run the model with [Haystack](https://github.com/deepset-ai/haystack/): |
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```python |
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# After running pip install haystack-ai "transformers[torch,sentencepiece]" |
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from haystack import Document |
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from haystack.components.readers import ExtractiveReader |
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docs = [ |
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Document(content="Python is a popular programming language"), |
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Document(content="python ist eine beliebte Programmiersprache"), |
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] |
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reader = ExtractiveReader(model="deepset/roberta-base-squad2-distilled") |
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reader.warm_up() |
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question = "What is a popular programming language?" |
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result = reader.run(query=question, documents=docs) |
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# {'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),...)]} |
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``` |
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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). |
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### In Transformers |
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```python |
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from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline |
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model_name = "deepset/roberta-base-squad2-distilled" |
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# a) Get predictions |
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nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) |
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QA_input = { |
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'question': 'Why is model conversion important?', |
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'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.' |
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} |
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res = nlp(QA_input) |
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|
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# b) Load model & tokenizer |
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model = AutoModelForQuestionAnswering.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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``` |
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## Performance |
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``` |
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"exact": 79.8366040596311 |
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"f1": 83.916407079888 |
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``` |
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|
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## Authors |
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**Timo M枚ller:** timo.moeller@deepset.ai |
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**Julian Risch:** julian.risch@deepset.ai |
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**Malte Pietsch:** malte.pietsch@deepset.ai |
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**Michel Bartels:** michel.bartels@deepset.ai |
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|
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## About us |
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<div class="grid lg:grid-cols-2 gap-x-4 gap-y-3"> |
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<div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> |
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<img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/deepset-logo-colored.png" class="w-40"/> |
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</div> |
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<div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> |
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<img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/haystack-logo-colored.png" class="w-40"/> |
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</div> |
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</div> |
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|
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[deepset](http://deepset.ai/) is the company behind the production-ready open-source AI framework [Haystack](https://haystack.deepset.ai/). |
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Some of our other work: |
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- [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")](https://huggingface.co/deepset/tinyroberta-squad2) |
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- [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) |
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- [deepset Cloud](https://www.deepset.ai/deepset-cloud-product), [deepset Studio](https://www.deepset.ai/deepset-studio) |
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## Get in touch and join the Haystack community |
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<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>. |
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We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community">Discord community open to everyone!</a></strong></p> |
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[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) |
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By the way: [we're hiring!](http://www.deepset.ai/jobs) |
