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dondosss
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rubert-finetuned-ner

Token Classification
Transformers
Safetensors
bert
Generated from Trainer
Inference Endpoints
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@@ -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 [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: 0.1466
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- - Precision: 0.8876
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- - Recall: 0.9075
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- - F1: 0.8975
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- - Accuracy: 0.9608
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  ## Model description
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@@ -43,26 +43,28 @@ More information needed
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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: 64
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- - eval_batch_size: 128
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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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- - num_epochs: 3
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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 | 313 | 0.1607 | 0.8568 | 0.8831 | 0.8697 | 0.9542 |
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- | 0.3113 | 2.0 | 626 | 0.1510 | 0.8780 | 0.9025 | 0.8901 | 0.9579 |
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- | 0.3113 | 3.0 | 939 | 0.1466 | 0.8876 | 0.9075 | 0.8975 | 0.9608 |
 
 
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  ### Framework versions
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- - Transformers 4.38.2
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  - Pytorch 2.2.1+cu121
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- - Datasets 2.18.0
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- - Tokenizers 0.15.2
 
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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: 0.2633
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+ - Precision: 0.7560
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+ - Recall: 0.8032
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+ - F1: 0.7789
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+ - Accuracy: 0.9251
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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: 16
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+ - eval_batch_size: 64
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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: cosine
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  - lr_scheduler_warmup_ratio: 0.05
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+ - num_epochs: 2
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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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+ | 0.4805 | 0.4 | 500 | 0.4017 | 0.6644 | 0.7072 | 0.6852 | 0.8788 |
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+ | 0.3281 | 0.8 | 1000 | 0.2818 | 0.7416 | 0.7886 | 0.7644 | 0.9203 |
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+ | 0.165 | 1.2 | 1500 | 0.2653 | 0.7573 | 0.8023 | 0.7792 | 0.9244 |
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+ | 0.2539 | 1.6 | 2000 | 0.2633 | 0.7571 | 0.8040 | 0.7799 | 0.9252 |
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+ | 0.252 | 2.0 | 2500 | 0.2633 | 0.7560 | 0.8032 | 0.7789 | 0.9251 |
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  ### Framework versions
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+ - Transformers 4.40.0
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  - Pytorch 2.2.1+cu121
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+ - Datasets 2.19.0
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+ - Tokenizers 0.19.1