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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: 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-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: 0.6639 |
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- Accuracy: 0.7637 |
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- Precision: 0.5304 |
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- Recall: 0.4710 |
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- F1: 0.4778 |
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- Ratio: 0.7903 |
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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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| 0.8468 | 0.07 | 10 | 0.8350 | 0.6185 | 0.3093 | 0.5 | 0.3822 | 1.0 | |
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| 0.7922 | 0.14 | 20 | 0.8314 | 0.6185 | 0.3093 | 0.5 | 0.3822 | 1.0 | |
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| 0.8005 | 0.21 | 30 | 0.8059 | 0.6169 | 0.2060 | 0.3325 | 0.2544 | 0.9984 | |
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| 0.8038 | 0.28 | 40 | 0.7907 | 0.6185 | 0.3093 | 0.5 | 0.3822 | 1.0 | |
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| 0.7846 | 0.34 | 50 | 0.8060 | 0.6185 | 0.3093 | 0.5 | 0.3822 | 1.0 | |
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| 0.7539 | 0.41 | 60 | 0.7573 | 0.6274 | 0.5024 | 0.3422 | 0.2763 | 0.9847 | |
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| 0.725 | 0.48 | 70 | 0.8018 | 0.7435 | 0.4978 | 0.4906 | 0.4940 | 0.5847 | |
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| 0.6842 | 0.55 | 80 | 0.8437 | 0.7419 | 0.5035 | 0.4795 | 0.4901 | 0.6444 | |
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| 0.7415 | 0.62 | 90 | 0.7783 | 0.7468 | 0.5006 | 0.4832 | 0.4909 | 0.6444 | |
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| 0.6303 | 0.69 | 100 | 0.7194 | 0.7452 | 0.5009 | 0.4723 | 0.4808 | 0.7040 | |
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| 0.6844 | 0.76 | 110 | 0.7137 | 0.7702 | 0.5106 | 0.4996 | 0.5044 | 0.6468 | |
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| 0.699 | 0.83 | 120 | 0.6666 | 0.7806 | 0.5159 | 0.5039 | 0.5084 | 0.6653 | |
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| 0.7229 | 0.9 | 130 | 0.6636 | 0.7629 | 0.5233 | 0.4730 | 0.4799 | 0.775 | |
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| 0.6555 | 0.97 | 140 | 0.6646 | 0.7637 | 0.5312 | 0.4707 | 0.4775 | 0.7919 | |
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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 |
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