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--- |
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license: apache-2.0 |
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base_model: projecte-aina/roberta-base-ca-v2-cawikitc |
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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: p1 |
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results: [] |
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pipeline_tag: zero-shot-classification |
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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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# p1 |
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This model is a fine-tuned version of [projecte-aina/roberta-base-ca-v2-cawikitc](https://huggingface.co/projecte-aina/roberta-base-ca-v2-cawikitc) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8254 |
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- Accuracy: 0.5 |
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- Precision: 0.25 |
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- Recall: 0.5 |
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- F1: 0.3333 |
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- Ratio: 1.0 |
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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: 16 |
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- eval_batch_size: 2 |
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- seed: 47 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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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: 2 |
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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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| 0.8263 | 0.38 | 10 | 0.8199 | 0.5 | 0.5 | 0.5 | 0.3473 | 0.0163 | |
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| 0.8283 | 0.75 | 20 | 0.8389 | 0.5 | 0.25 | 0.5 | 0.3333 | 1.0 | |
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| 0.8167 | 1.13 | 30 | 0.8325 | 0.5 | 0.25 | 0.5 | 0.3333 | 1.0 | |
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| 0.8183 | 1.51 | 40 | 0.8228 | 0.4973 | 0.2493 | 0.4973 | 0.3321 | 0.9973 | |
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| 0.8178 | 1.89 | 50 | 0.8254 | 0.5 | 0.25 | 0.5 | 0.3333 | 1.0 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.15.2 |
