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--- |
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license: mit |
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datasets: |
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- squad_v2 |
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language: |
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- en |
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tags: |
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- bart |
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- question-answering |
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--- |
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# bart-base for QA |
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This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. |
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## Overview |
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**Language model:** bart-base |
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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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**Infrastructure**: 1x NVIDIA 3070 |
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## Model Usage |
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```python |
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from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline |
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model_name = "sjrhuschlee/bart-base-squad2" |
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# a) Using pipelines |
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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': 'Where do I live?', |
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'context': 'My name is Sarah and I live in London' |
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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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## Metrics |
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More information needed. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-06 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 6 |
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- total_train_batch_size: 96 |
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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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### Framework versions |
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- Transformers 4.30.0.dev0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
