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--- |
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language: |
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- en |
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license: mit |
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tags: |
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- bart |
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- question-answering |
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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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base_model: facebook/bart-base |
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model-index: |
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- name: sjrhuschlee/bart-base-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: 75.223 |
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name: Exact Match |
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- type: f1 |
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value: 78.443 |
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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: 83.406 |
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name: Exact Match |
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- type: f1 |
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value: 90.377 |
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name: F1 |
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--- |
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# bart-base for Extractive 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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```bash |
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# Squad v2 |
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{ |
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"eval_HasAns_exact": 76.45074224021593, |
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"eval_HasAns_f1": 82.88605283171232, |
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"eval_HasAns_total": 5928, |
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"eval_NoAns_exact": 74.01177460050462, |
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"eval_NoAns_f1": 74.01177460050462, |
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"eval_NoAns_total": 5945, |
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"eval_best_exact": 75.23793481007327, |
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"eval_best_exact_thresh": 0.0, |
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"eval_best_f1": 78.45098300230696, |
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"eval_best_f1_thresh": 0.0, |
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"eval_exact": 75.22951233892024, |
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"eval_f1": 78.44256053115387, |
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"eval_runtime": 131.875, |
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"eval_samples": 11955, |
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"eval_samples_per_second": 90.654, |
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"eval_steps_per_second": 3.784, |
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"eval_total": 11873 |
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} |
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# Squad |
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{ |
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"eval_exact_match": 83.40586565752129, |
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"eval_f1": 90.37706849113668, |
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"eval_runtime": 117.2093, |
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"eval_samples": 10619, |
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"eval_samples_per_second": 90.599, |
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"eval_steps_per_second": 3.78 |
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} |
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``` |
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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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- max_seq_length 512 |
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- doc_stride 128 |
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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: Adam8Bit 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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- gradient_checkpointing: True |
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- tf32: True |
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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 |
