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BioMedRoBERTa-full-finetuned-ner-pablo

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  1. README.md +18 -16
  2. model.safetensors +1 -1
  3. runs/Aug23_14-36-40_ee1898c059d7/events.out.tfevents.1724423801.ee1898c059d7.1664.9 +2 -2
README.md CHANGED
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [allenai/biomed_roberta_base](https://huggingface.co/allenai/biomed_roberta_base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0985
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- - Precision: 0.8190
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- - Recall: 0.8095
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- - F1: 0.8142
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- - Accuracy: 0.9779
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  ## Model description
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@@ -43,10 +43,12 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 3e-05
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- - train_batch_size: 2
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- - eval_batch_size: 2
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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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  - lr_scheduler_warmup_ratio: 0.05
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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- |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 0.0864 | 1.0 | 2509 | 0.0915 | 0.8408 | 0.7476 | 0.7915 | 0.9753 |
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- | 0.0616 | 2.0 | 5018 | 0.0766 | 0.8116 | 0.7911 | 0.8012 | 0.9782 |
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- | 0.0439 | 3.0 | 7527 | 0.0838 | 0.8132 | 0.8024 | 0.8078 | 0.9776 |
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- | 0.0338 | 4.0 | 10036 | 0.0891 | 0.8207 | 0.8030 | 0.8118 | 0.9775 |
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- | 0.0268 | 5.0 | 12545 | 0.0985 | 0.8190 | 0.8095 | 0.8142 | 0.9779 |
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  ### Framework versions
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- - Transformers 4.44.1
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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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  This model is a fine-tuned version of [allenai/biomed_roberta_base](https://huggingface.co/allenai/biomed_roberta_base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0951
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+ - Precision: 0.8139
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+ - Recall: 0.8085
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+ - F1: 0.8112
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+ - Accuracy: 0.9769
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  ## Model description
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  ### Training hyperparameters
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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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  ### Training results
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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 | 0.0910 | 0.7348 | 0.7922 | 0.7624 | 0.9723 |
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+ | 0.19 | 1.9980 | 505 | 0.0808 | 0.8030 | 0.7908 | 0.7969 | 0.9768 |
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+ | 0.19 | 2.9990 | 758 | 0.0802 | 0.8125 | 0.7932 | 0.8028 | 0.9768 |
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+ | 0.0433 | 4.0 | 1011 | 0.0865 | 0.8131 | 0.8103 | 0.8117 | 0.9772 |
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+ | 0.0433 | 4.9852 | 1260 | 0.0951 | 0.8139 | 0.8085 | 0.8112 | 0.9769 |
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  ### Framework versions
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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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