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--- |
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license: apache-2.0 |
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base_model: projecte-aina/roberta-base-ca-v2-cased-te |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: 2404v5 |
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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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# 2404v5 |
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This model is a fine-tuned version of [projecte-aina/roberta-base-ca-v2-cased-te](https://huggingface.co/projecte-aina/roberta-base-ca-v2-cased-te) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6586 |
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- Accuracy: 0.8403 |
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- Precision: 0.8407 |
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- Recall: 0.8403 |
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- F1: 0.8403 |
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- Ratio: 0.5168 |
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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: 10 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 20 |
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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: 0.06 |
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- lr_scheduler_warmup_steps: 4 |
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- num_epochs: 4 |
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- label_smoothing_factor: 0.1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Ratio | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:| |
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| 3.0551 | 0.1626 | 10 | 1.7145 | 0.5 | 0.5 | 0.5 | 0.3733 | 0.0504 | |
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| 1.0346 | 0.3252 | 20 | 0.8860 | 0.5336 | 0.5558 | 0.5336 | 0.4822 | 0.8151 | |
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| 0.8531 | 0.4878 | 30 | 0.8174 | 0.5672 | 0.6156 | 0.5672 | 0.5166 | 0.1765 | |
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| 0.8279 | 0.6504 | 40 | 0.7147 | 0.7563 | 0.7928 | 0.7563 | 0.7485 | 0.6765 | |
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| 0.744 | 0.8130 | 50 | 0.6403 | 0.8067 | 0.8099 | 0.8067 | 0.8062 | 0.5504 | |
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| 0.6594 | 0.9756 | 60 | 0.6299 | 0.7983 | 0.8004 | 0.7983 | 0.7980 | 0.5420 | |
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| 0.5973 | 1.1382 | 70 | 0.6320 | 0.8193 | 0.8204 | 0.8193 | 0.8192 | 0.5294 | |
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| 0.5934 | 1.3008 | 80 | 0.6306 | 0.8151 | 0.8184 | 0.8151 | 0.8147 | 0.4496 | |
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| 0.5529 | 1.4634 | 90 | 0.6442 | 0.8193 | 0.8195 | 0.8193 | 0.8193 | 0.5126 | |
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| 0.5618 | 1.6260 | 100 | 0.6186 | 0.8193 | 0.8204 | 0.8193 | 0.8192 | 0.5294 | |
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| 0.5632 | 1.7886 | 110 | 0.5845 | 0.8361 | 0.8373 | 0.8361 | 0.8360 | 0.5294 | |
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| 0.5886 | 1.9512 | 120 | 0.5752 | 0.8361 | 0.8367 | 0.8361 | 0.8361 | 0.5210 | |
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| 0.5596 | 2.1138 | 130 | 0.5760 | 0.8403 | 0.8407 | 0.8403 | 0.8403 | 0.5168 | |
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| 0.4964 | 2.2764 | 140 | 0.6181 | 0.8361 | 0.8367 | 0.8361 | 0.8361 | 0.4790 | |
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| 0.5014 | 2.4390 | 150 | 0.6422 | 0.8361 | 0.8381 | 0.8361 | 0.8359 | 0.5378 | |
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| 0.5251 | 2.6016 | 160 | 0.6033 | 0.8403 | 0.8428 | 0.8403 | 0.8401 | 0.5420 | |
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| 0.4723 | 2.7642 | 170 | 0.5839 | 0.8487 | 0.8503 | 0.8487 | 0.8486 | 0.5336 | |
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| 0.4864 | 2.9268 | 180 | 0.5837 | 0.8613 | 0.8616 | 0.8613 | 0.8613 | 0.5126 | |
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| 0.4512 | 3.0894 | 190 | 0.5973 | 0.8487 | 0.8491 | 0.8487 | 0.8487 | 0.5168 | |
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| 0.477 | 3.2520 | 200 | 0.6159 | 0.8403 | 0.8404 | 0.8403 | 0.8403 | 0.5084 | |
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| 0.4198 | 3.4146 | 210 | 0.6523 | 0.8403 | 0.8407 | 0.8403 | 0.8403 | 0.5168 | |
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| 0.4322 | 3.5772 | 220 | 0.6646 | 0.8403 | 0.8407 | 0.8403 | 0.8403 | 0.5168 | |
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| 0.4889 | 3.7398 | 230 | 0.6632 | 0.8403 | 0.8407 | 0.8403 | 0.8403 | 0.5168 | |
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| 0.4409 | 3.9024 | 240 | 0.6589 | 0.8403 | 0.8407 | 0.8403 | 0.8403 | 0.5168 | |
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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 |
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