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ekaterinatao
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nerel-bio-RuBioBERT-base

Token Classification
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bert
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  1. README.md +17 -17
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -19,11 +19,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [alexyalunin/RuBioBERT](https://huggingface.co/alexyalunin/RuBioBERT) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5886
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- - Precision: 0.8036
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- - Recall: 0.8068
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- - F1: 0.8051
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- - Accuracy: 0.8673
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 5e-05
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- - train_batch_size: 6
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- - eval_batch_size: 6
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  - seed: 64
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: cosine
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | No log | 1.0 | 102 | 1.0315 | 0.6748 | 0.6531 | 0.6638 | 0.7545 |
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- | No log | 2.0 | 204 | 0.5981 | 0.7803 | 0.7662 | 0.7732 | 0.8395 |
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- | No log | 3.0 | 306 | 0.5266 | 0.7899 | 0.7953 | 0.7926 | 0.8605 |
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- | No log | 4.0 | 408 | 0.5254 | 0.7909 | 0.7831 | 0.7869 | 0.8564 |
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- | 0.7299 | 5.0 | 510 | 0.5269 | 0.8067 | 0.8004 | 0.8035 | 0.8636 |
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- | 0.7299 | 6.0 | 612 | 0.5618 | 0.7996 | 0.7971 | 0.7984 | 0.8626 |
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- | 0.7299 | 7.0 | 714 | 0.5589 | 0.8067 | 0.8075 | 0.8071 | 0.8684 |
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- | 0.7299 | 8.0 | 816 | 0.5737 | 0.7998 | 0.8021 | 0.8009 | 0.8642 |
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- | 0.7299 | 9.0 | 918 | 0.5857 | 0.8065 | 0.8093 | 0.8079 | 0.8692 |
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- | 0.0753 | 10.0 | 1020 | 0.5886 | 0.8036 | 0.8068 | 0.8051 | 0.8673 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [alexyalunin/RuBioBERT](https://huggingface.co/alexyalunin/RuBioBERT) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6187
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+ - Precision: 0.8107
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+ - Recall: 0.8157
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+ - F1: 0.8132
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+ - Accuracy: 0.8721
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 5e-05
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+ - train_batch_size: 4
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+ - eval_batch_size: 4
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  - seed: 64
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: cosine
 
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 1.0 | 153 | 0.8937 | 0.7363 | 0.7078 | 0.7218 | 0.7998 |
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+ | No log | 2.0 | 306 | 0.5753 | 0.7907 | 0.7798 | 0.7852 | 0.8527 |
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+ | No log | 3.0 | 459 | 0.5308 | 0.7893 | 0.7856 | 0.7875 | 0.8559 |
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+ | 0.9025 | 4.0 | 612 | 0.5477 | 0.7956 | 0.7962 | 0.7959 | 0.8618 |
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+ | 0.9025 | 5.0 | 765 | 0.5732 | 0.8011 | 0.8037 | 0.8024 | 0.8646 |
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+ | 0.9025 | 6.0 | 918 | 0.5551 | 0.8085 | 0.8082 | 0.8084 | 0.8693 |
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+ | 0.133 | 7.0 | 1071 | 0.5813 | 0.8082 | 0.8082 | 0.8082 | 0.8701 |
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+ | 0.133 | 8.0 | 1224 | 0.6006 | 0.8053 | 0.8131 | 0.8092 | 0.8687 |
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+ | 0.133 | 9.0 | 1377 | 0.6183 | 0.8104 | 0.8159 | 0.8131 | 0.8723 |
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+ | 0.0406 | 10.0 | 1530 | 0.6187 | 0.8107 | 0.8157 | 0.8132 | 0.8721 |
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  ### Framework versions
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