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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: 0.6733 |
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- Accuracy: 0.8109 |
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- Precision: 0.8127 |
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- Recall: 0.8109 |
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- F1: 0.8107 |
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- Ratio: 0.5378 |
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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: 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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| 3.8156 | 0.1626 | 10 | 1.9553 | 0.5378 | 0.5507 | 0.5378 | 0.5064 | 0.7521 | |
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| 1.3339 | 0.3252 | 20 | 1.2090 | 0.5546 | 0.5548 | 0.5546 | 0.5543 | 0.5252 | |
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| 1.103 | 0.4878 | 30 | 0.9577 | 0.5588 | 0.5588 | 0.5588 | 0.5588 | 0.5042 | |
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| 0.9108 | 0.6504 | 40 | 0.8881 | 0.5714 | 0.5770 | 0.5714 | 0.5635 | 0.6345 | |
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| 0.8716 | 0.8130 | 50 | 0.8426 | 0.6387 | 0.6563 | 0.6387 | 0.6282 | 0.6681 | |
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| 0.844 | 0.9756 | 60 | 0.7948 | 0.7017 | 0.7233 | 0.7017 | 0.6943 | 0.3445 | |
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| 0.7816 | 1.1382 | 70 | 0.7715 | 0.7227 | 0.7660 | 0.7227 | 0.7109 | 0.7017 | |
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| 0.7406 | 1.3008 | 80 | 0.7040 | 0.8067 | 0.8099 | 0.8067 | 0.8062 | 0.5504 | |
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| 0.6764 | 1.4634 | 90 | 0.6954 | 0.8025 | 0.8104 | 0.8025 | 0.8013 | 0.5798 | |
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| 0.7306 | 1.6260 | 100 | 0.6933 | 0.8109 | 0.8209 | 0.8109 | 0.8094 | 0.5882 | |
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| 0.6736 | 1.7886 | 110 | 0.6763 | 0.8067 | 0.8089 | 0.8067 | 0.8064 | 0.5420 | |
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| 0.714 | 1.9512 | 120 | 0.6733 | 0.8109 | 0.8127 | 0.8109 | 0.8107 | 0.5378 | |
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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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