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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_ita |
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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_ita |
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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: 0.6432 |
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- F1: 0.7407 |
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- Roc Auc: 0.7843 |
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- Accuracy: 0.6735 |
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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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| 0.5508 | 1.0 | 1037 | 0.5281 | 0.6704 | 0.7301 | 0.6151 | |
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| 0.4816 | 2.0 | 2074 | 0.4964 | 0.7109 | 0.7631 | 0.6770 | |
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| 0.4004 | 3.0 | 3111 | 0.5246 | 0.7299 | 0.7743 | 0.6701 | |
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| 0.3353 | 4.0 | 4148 | 0.5791 | 0.7310 | 0.7767 | 0.6718 | |
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| 0.2731 | 5.0 | 5185 | 0.6432 | 0.7407 | 0.7843 | 0.6735 | |
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| 0.2271 | 6.0 | 6222 | 0.6896 | 0.7248 | 0.7710 | 0.6460 | |
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| 0.1842 | 7.0 | 7259 | 0.7441 | 0.7271 | 0.7757 | 0.6667 | |
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| 0.15 | 8.0 | 8296 | 0.8640 | 0.7301 | 0.7769 | 0.6632 | |
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| 0.1123 | 9.0 | 9333 | 0.9447 | 0.7356 | 0.7812 | 0.6701 | |
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| 0.0973 | 10.0 | 10370 | 0.9652 | 0.7310 | 0.7773 | 0.6632 | |
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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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