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--- |
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base_model: DeepPavlov/rubert-base-cased |
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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: nerel-bio-rubert-base |
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results: [] |
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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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# nerel-bio-rubert-base |
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This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6122 |
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- Precision: 0.7873 |
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- Recall: 0.7882 |
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- F1: 0.7878 |
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- Accuracy: 0.8601 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 10 |
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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 | 1.0 | 102 | 1.1211 | 0.6196 | 0.5809 | 0.5996 | 0.7125 | |
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| No log | 2.0 | 204 | 0.6800 | 0.7333 | 0.7165 | 0.7248 | 0.8137 | |
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| No log | 3.0 | 306 | 0.5985 | 0.7445 | 0.7488 | 0.7466 | 0.8303 | |
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| No log | 4.0 | 408 | 0.5673 | 0.7608 | 0.7622 | 0.7615 | 0.8402 | |
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| 0.7954 | 5.0 | 510 | 0.5665 | 0.7751 | 0.7702 | 0.7726 | 0.8485 | |
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| 0.7954 | 6.0 | 612 | 0.5934 | 0.7826 | 0.7742 | 0.7784 | 0.8544 | |
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| 0.7954 | 7.0 | 714 | 0.5804 | 0.7795 | 0.7751 | 0.7773 | 0.8527 | |
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| 0.7954 | 8.0 | 816 | 0.6075 | 0.7839 | 0.7878 | 0.7858 | 0.8577 | |
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| 0.7954 | 9.0 | 918 | 0.6139 | 0.7887 | 0.7889 | 0.7888 | 0.8614 | |
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| 0.1024 | 10.0 | 1020 | 0.6122 | 0.7873 | 0.7882 | 0.7878 | 0.8601 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.2 |
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