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--- |
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language: en |
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license: cc-by-4.0 |
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base_model: google/flan-t5-xl |
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tags: |
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- question-answering |
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- flan |
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- flan-t5 |
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- squad |
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- squad_v2 |
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datasets: |
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- squad_v2 |
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- squad |
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model-index: |
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- name: deepset/flan-t5-xl-squad2 |
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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: 88.790 |
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name: Exact Match |
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- type: f1 |
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value: 91.617 |
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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 |
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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: 90.331 |
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name: Exact Match |
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- type: f1 |
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value: 95.722 |
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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: 54.367 |
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name: Exact Match |
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- type: f1 |
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value: 68.055 |
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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: 87.241 |
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name: Exact Match |
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- type: f1 |
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value: 92.894 |
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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: 77.602 |
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name: Exact Match |
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- type: f1 |
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value: 90.426 |
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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: 85.639 |
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name: Exact Match |
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- type: f1 |
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value: 93.974 |
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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: 87.392 |
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name: Exact Match |
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- type: f1 |
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value: 94.579 |
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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: 79.323 |
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name: Exact Match |
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- type: f1 |
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value: 90.083 |
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name: F1 |
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--- |
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# flan-t5-xl for Extractive QA |
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This is the [flan-t5-xl](https://huggingface.co/google/flan-t5-xl) 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 Extractive Question Answering. |
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## Overview |
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**Language model:** flan-t5-xl |
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**Language:** English |
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**Downstream-task:** Extractive QA |
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**Training data:** SQuAD 2.0 |
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**Eval data:** SQuAD 2.0 |
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**Code:** See [an example QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/first-qa-system) |
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## Hyperparameters |
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``` |
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learning_rate: 1e-05 |
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train_batch_size: 4 |
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eval_batch_size: 8 |
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seed: 42 |
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gradient_accumulation_steps: 16 |
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total_train_batch_size: 64 |
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optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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lr_scheduler_type: linear |
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lr_scheduler_warmup_ratio: 0.1 |
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num_epochs: 4.0 |
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``` |
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## Usage |
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### In Haystack |
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Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do extractive question answering at scale (over many documents). To load the model in [Haystack](https://github.com/deepset-ai/haystack/): |
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```python |
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# NOTE: This only works with Haystack v2.0! |
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reader = ExtractiveReader("deepset/flan-t5-xl-squad2") |
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``` |
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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/flan-t5-xl-squad2" |
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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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# 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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## Authors |
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**Sebastian Husch Lee:** sebastian.huschlee [at] deepset.ai |
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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://huggingface.co/spaces/deepset/README/resolve/main/haystack-logo-colored.svg" 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://huggingface.co/spaces/deepset/README/resolve/main/deepset-logo-colored.svg" class="w-40"/> |
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</div> |
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</div> |
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[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. |
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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://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/join">Discord community open to everyone!</a></strong></p> |
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[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) |
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