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--- |
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base_model: Musixmatch/umberto-commoncrawl-cased-v1 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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- accuracy |
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model-index: |
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- name: target_classification_ita2 |
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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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# target_classification_ita2 |
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This model is a fine-tuned version of [Musixmatch/umberto-commoncrawl-cased-v1](https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3451 |
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- F1: 0.4422 |
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- Roc Auc: 0.6814 |
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- Accuracy: 0.8127 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| |
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| 0.4759 | 1.0 | 897 | 0.2585 | 0.0794 | 0.5170 | 0.8093 | |
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| 0.3547 | 2.0 | 1794 | 0.2457 | 0.2517 | 0.5753 | 0.8230 | |
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| 0.2756 | 3.0 | 2691 | 0.2561 | 0.4138 | 0.6693 | 0.8007 | |
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| 0.1996 | 4.0 | 3588 | 0.2704 | 0.3929 | 0.6400 | 0.8299 | |
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| 0.1529 | 5.0 | 4485 | 0.3451 | 0.4422 | 0.6814 | 0.8127 | |
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| 0.1057 | 6.0 | 5382 | 0.4442 | 0.3605 | 0.6279 | 0.8196 | |
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| 0.0644 | 7.0 | 6279 | 0.4965 | 0.3858 | 0.6518 | 0.7973 | |
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| 0.045 | 8.0 | 7176 | 0.5365 | 0.3867 | 0.6440 | 0.8179 | |
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| 0.0328 | 9.0 | 8073 | 0.5268 | 0.4046 | 0.6478 | 0.8316 | |
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| 0.0275 | 10.0 | 8970 | 0.5573 | 0.4044 | 0.6533 | 0.8213 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.3.0+cu118 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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