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BioBERT-full-finetuned-ner-pablo

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  1. README.md +74 -71
  2. runs/Aug23_14-03-21_ee1898c059d7/events.out.tfevents.1724421802.ee1898c059d7.1664.5 +2 -2
README.md CHANGED
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- ---
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- base_model: dmis-lab/biobert-base-cased-v1.2
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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: BioBERT-full-finetuned-ner-pablo
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- results: []
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- ---
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-
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- <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- should probably proofread and complete it, then remove this comment. -->
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-
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- # BioBERT-full-finetuned-ner-pablo
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-
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- This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on the None dataset.
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- It achieves the following results on the evaluation set:
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- - Loss: 0.1114
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- - Precision: 0.7951
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- - Recall: 0.7809
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- - F1: 0.7879
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- - Accuracy: 0.9690
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-
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- ## Model description
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-
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- More information needed
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-
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- ## Intended uses & limitations
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-
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- More information needed
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-
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- ## Training and evaluation data
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-
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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: 0.0002
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- - train_batch_size: 4
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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: 16
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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: 0.05
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- - num_epochs: 3
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- - mixed_precision_training: Native AMP
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-
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- ### Training results
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-
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- | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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- |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 0.1541 | 0.9998 | 2608 | 0.1456 | 0.6888 | 0.7147 | 0.7015 | 0.9601 |
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- | 0.1073 | 2.0 | 5217 | 0.1244 | 0.7397 | 0.7450 | 0.7423 | 0.9645 |
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- | 0.0744 | 2.9994 | 7824 | 0.1114 | 0.7951 | 0.7809 | 0.7879 | 0.9690 |
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-
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-
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- ### Framework versions
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-
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- - Transformers 4.44.0
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- - Pytorch 2.4.0+cu124
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- - Datasets 2.21.0
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- - Tokenizers 0.19.1
 
 
 
 
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+ ---
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+ library_name: transformers
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+ base_model: dmis-lab/biobert-base-cased-v1.2
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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: BioBERT-full-finetuned-ner-pablo
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # BioBERT-full-finetuned-ner-pablo
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+
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+ This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1081
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+ - Precision: 0.8057
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+ - Recall: 0.8003
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+ - F1: 0.8030
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+ - Accuracy: 0.9743
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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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: 0.0002
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 64
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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: 0.05
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+ - num_epochs: 5
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 0.9970 | 252 | 0.0943 | 0.7586 | 0.7859 | 0.7720 | 0.9732 |
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+ | 0.1716 | 1.9980 | 505 | 0.0917 | 0.7950 | 0.7738 | 0.7843 | 0.9745 |
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+ | 0.1716 | 2.9990 | 758 | 0.0886 | 0.7956 | 0.7925 | 0.7940 | 0.9742 |
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+ | 0.0465 | 4.0 | 1011 | 0.0956 | 0.8055 | 0.7971 | 0.8013 | 0.9743 |
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+ | 0.0465 | 4.9852 | 1260 | 0.1081 | 0.8057 | 0.8003 | 0.8030 | 0.9743 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.44.2
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+ - Pytorch 2.4.0+cu121
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+ - Datasets 2.21.0
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+ - Tokenizers 0.19.1
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