Model save
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- training_args.bin +1 -1
README.md
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base_model: surrey-nlp/roberta-base-finetuned-abbr
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tags:
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- generated_from_trainer
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: bert-base-NER-finetuned-ner
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results: []
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# bert-base-NER-finetuned-ner
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This model is a fine-tuned version of [surrey-nlp/roberta-base-finetuned-abbr](https://huggingface.co/surrey-nlp/roberta-base-finetuned-abbr) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4915
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- Precision: 0.8180
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- Recall: 0.8640
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- F1: 0.8404
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- Accuracy: 0.8172
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## Model description
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size:
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- eval_batch_size: 4
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- seed: 42
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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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- num_epochs:
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### Training results
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### Framework versions
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base_model: surrey-nlp/roberta-base-finetuned-abbr
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tags:
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- generated_from_trainer
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model-index:
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- name: bert-base-NER-finetuned-ner
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results: []
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# bert-base-NER-finetuned-ner
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This model is a fine-tuned version of [surrey-nlp/roberta-base-finetuned-abbr](https://huggingface.co/surrey-nlp/roberta-base-finetuned-abbr) on an unknown dataset.
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## Model description
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 8
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- eval_batch_size: 4
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 32
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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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- num_epochs: 20
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- mixed_precision_training: Native AMP
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### Framework versions
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model.safetensors
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training_args.bin
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