End of training

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+model-index:
+- name: lilt-ruroberta
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# lilt-ruroberta
+
+This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.7493
+- Comment: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}
+- Date: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 23}
+- Labname: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 18}
+- Laboratory: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
+- Measure: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5}
+- Ref Value: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}
+- Result: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}
+- Overall Precision: 0.0
+- Overall Recall: 0.0
+- Overall F1: 0.0
+- Overall Accuracy: 0.375
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- training_steps: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Comment                                                                                     | Date                                                                                                       | Labname                                                                                                   | Laboratory                                                | Measure                                                   | Ref Value                                                  | Result                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:----------------------------------------------------------:|:---------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 2.6158        | 0.5   | 1    | 2.6467          | {'precision': 0.06666666666666667, 'recall': 0.375, 'f1': 0.11320754716981134, 'number': 8} | {'precision': 0.13333333333333333, 'recall': 0.17391304347826086, 'f1': 0.15094339622641512, 'number': 23} | {'precision': 0.16666666666666666, 'recall': 0.1111111111111111, 'f1': 0.13333333333333333, 'number': 18} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.0657            | 0.1324         | 0.0878     | 0.0375           |
+| 2.6704        | 1.0   | 2    | 2.6467          | {'precision': 0.06666666666666667, 'recall': 0.375, 'f1': 0.11320754716981134, 'number': 8} | {'precision': 0.13333333333333333, 'recall': 0.17391304347826086, 'f1': 0.15094339622641512, 'number': 23} | {'precision': 0.16666666666666666, 'recall': 0.1111111111111111, 'f1': 0.13333333333333333, 'number': 18} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.0657            | 0.1324         | 0.0878     | 0.0375           |
+| 2.6164        | 1.5   | 3    | 2.6467          | {'precision': 0.06666666666666667, 'recall': 0.375, 'f1': 0.11320754716981134, 'number': 8} | {'precision': 0.13333333333333333, 'recall': 0.17391304347826086, 'f1': 0.15094339622641512, 'number': 23} | {'precision': 0.16666666666666666, 'recall': 0.1111111111111111, 'f1': 0.13333333333333333, 'number': 18} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.0657            | 0.1324         | 0.0878     | 0.0375           |
+| 2.6707        | 2.0   | 4    | 2.2168          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}                                   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 23}                                                 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 18}                                                | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.0               | 0.0            | 0.0        | 0.375            |
+| 1.8689        | 2.5   | 5    | 2.1469          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}                                   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 23}                                                 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 18}                                                | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.0               | 0.0            | 0.0        | 0.375            |
+| 2.1588        | 3.0   | 6    | 1.9684          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}                                   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 23}                                                 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 18}                                                | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.0               | 0.0            | 0.0        | 0.375            |
+| 1.0594        | 3.5   | 7    | 2.0123          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}                                   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 23}                                                 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 18}                                                | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.0               | 0.0            | 0.0        | 0.375            |
+| 2.0705        | 4.0   | 8    | 1.8896          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}                                   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 23}                                                 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 18}                                                | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.0               | 0.0            | 0.0        | 0.375            |
+| 1.9698        | 4.5   | 9    | 1.7493          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}                                   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 23}                                                 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 18}                                                | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.0               | 0.0            | 0.0        | 0.375            |
+| 0.8502        | 5.0   | 10   | 1.6972          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}                                   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 23}                                                 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 18}                                                | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.0               | 0.0            | 0.0        | 0.375            |
+
+
+### Framework versions
+
+- Transformers 4.25.1
+- Pytorch 1.12.1
+- Datasets 2.8.0
+- Tokenizers 0.13.2

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preprocessor_config.json

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special_tokens_map.json

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tokenizer_config.json

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