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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: stocks |
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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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# stocks |
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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: 0.6453 |
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- Accuracy: 0.8309 |
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- Precision: 0.8362 |
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- Recall: 0.8309 |
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- F1: 0.8302 |
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- Ratio: 0.5631 |
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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: 2e-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: 1 |
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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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| 0.5106 | 0.1626 | 10 | 0.6599 | 0.8289 | 0.8500 | 0.8289 | 0.8262 | 0.3772 | |
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| 0.5228 | 0.3252 | 20 | 0.5642 | 0.8517 | 0.8517 | 0.8517 | 0.8517 | 0.5020 | |
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| 0.5035 | 0.4878 | 30 | 0.5669 | 0.8544 | 0.8554 | 0.8544 | 0.8543 | 0.4725 | |
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| 0.4325 | 0.6504 | 40 | 0.6077 | 0.8403 | 0.8442 | 0.8403 | 0.8398 | 0.4463 | |
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| 0.4902 | 0.8130 | 50 | 0.6391 | 0.8322 | 0.8369 | 0.8322 | 0.8316 | 0.5591 | |
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| 0.4774 | 0.9756 | 60 | 0.6453 | 0.8309 | 0.8362 | 0.8309 | 0.8302 | 0.5631 | |
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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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