Model save

README.md

@@ -19,9 +19,9 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.3824
-- Accuracy: 0.9121
-- F1: 0.9120
+- Loss: 0.5161
+- Accuracy: 0.8938
+- F1: 0.8910
 
 ## Model description
 
@@ -41,25 +41,28 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 2e-05
-- train_batch_size: 8
-- eval_batch_size: 8
+- train_batch_size: 16
+- eval_batch_size: 16
 - seed: 42
-- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: linear
-- num_epochs: 3
+- num_epochs: 5
+- mixed_precision_training: Native AMP
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
-| 0.5257        | 1.0   | 102  | 0.3601          | 0.9118   | 0.9174 |
-| 0.2449        | 2.0   | 204  | 0.3734          | 0.9314   | 0.9381 |
-| 0.2091        | 3.0   | 306  | 0.3486          | 0.9314   | 0.9369 |
+| 0.6300        | 1.0   | 80   | 0.3917          | 0.8516   | 0.8508 |
+| 0.3762        | 2.0   | 160  | 0.4943          | 0.8132   | 0.7866 |
+| 0.3336        | 3.0   | 240  | 0.4476          | 0.8773   | 0.8743 |
+| 0.2274        | 4.0   | 320  | 0.3879          | 0.9048   | 0.9041 |
+| 0.2204        | 5.0   | 400  | 0.5161          | 0.8938   | 0.8910 |
 
 
 ### Framework versions
 
-- Transformers 4.57.5
-- Pytorch 2.9.1+cpu
+- Transformers 5.0.0
+- Pytorch 2.7.1+cu118
 - Datasets 4.5.0
 - Tokenizers 0.22.2

model.safetensors

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tokenizer.json

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tokenizer_config.json

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   "eos_token": "</s>",
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