--- language: en license: cc-by-4.0 base_model: google/flan-t5-xl tags: - question-answering - flan - flan-t5 - squad - squad_v2 datasets: - squad_v2 - squad model-index: - name: deepset/flan-t5-xl-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: 88.790 name: Exact Match - type: f1 value: 91.617 name: F1 - task: type: question-answering name: Question Answering dataset: name: squad type: squad config: plain_text split: validation metrics: - type: exact_match value: 90.331 name: Exact Match - type: f1 value: 95.722 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: 54.367 name: Exact Match - type: f1 value: 68.055 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: 87.241 name: Exact Match - type: f1 value: 92.894 name: F1 - task: type: question-answering name: Question Answering dataset: name: squadshifts amazon type: squadshifts config: amazon split: test metrics: - type: exact_match value: 77.602 name: Exact Match - type: f1 value: 90.426 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: 85.639 name: Exact Match - type: f1 value: 93.974 name: F1 - task: type: question-answering name: Question Answering dataset: name: squadshifts nyt type: squadshifts config: nyt split: test metrics: - type: exact_match value: 87.392 name: Exact Match - type: f1 value: 94.579 name: F1 - task: type: question-answering name: Question Answering dataset: name: squadshifts reddit type: squadshifts config: reddit split: test metrics: - type: exact_match value: 79.323 name: Exact Match - type: f1 value: 90.083 name: F1 --- # flan-t5-xl for Extractive QA 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. ## Overview **Language model:** flan-t5-xl **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Code:** See [an example QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/first-qa-system) ## Hyperparameters ``` learning_rate: 1e-05 train_batch_size: 4 eval_batch_size: 8 seed: 42 gradient_accumulation_steps: 16 total_train_batch_size: 64 optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 lr_scheduler_type: linear lr_scheduler_warmup_ratio: 0.1 num_epochs: 4.0 ``` ## Usage ### In Haystack 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/): ```python # NOTE: This only works with Haystack v2.0! reader = ExtractiveReader(model_name_or_path="deepset/flan-t5-xl-squad2") ``` ### In Transformers ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "deepset/flan-t5-xl-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 Husch Lee:** sebastian.huschlee [at] deepset.ai ## About us
[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. ## Get in touch and join the Haystack community

For more info on Haystack, visit our GitHub repo and Documentation. We also have a Discord community open to everyone!

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