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  license: mit
 
 
 
 
 
 
 
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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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+
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+ # bart-base for QA
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+
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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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+
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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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+
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+
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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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+
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+ ## Metrics
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+ More information needed.
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+
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+ ### Framework versions
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+
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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