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--- |
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base_model: MilaNLProc/feel-it-italian-emotion |
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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: feel_it_finetuned_pro_emit_correlations |
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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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# feel_it_finetuned_pro_emit_correlations |
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This model is a fine-tuned version of [MilaNLProc/feel-it-italian-emotion](https://huggingface.co/MilaNLProc/feel-it-italian-emotion) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3170 |
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- F1: 0.4564 |
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- Roc Auc: 0.6868 |
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- Accuracy: 0.3162 |
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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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| 0.309 | 1.0 | 1037 | 0.2952 | 0.1169 | 0.5394 | 0.2079 | |
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| 0.2586 | 2.0 | 2074 | 0.2731 | 0.2947 | 0.6109 | 0.2766 | |
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| 0.2142 | 3.0 | 3111 | 0.2721 | 0.4023 | 0.6552 | 0.3024 | |
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| 0.1829 | 4.0 | 4148 | 0.2731 | 0.4274 | 0.6637 | 0.3076 | |
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| 0.1564 | 5.0 | 5185 | 0.2862 | 0.4288 | 0.6700 | 0.3110 | |
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| 0.1341 | 6.0 | 6222 | 0.3004 | 0.4502 | 0.6826 | 0.3024 | |
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| 0.1187 | 7.0 | 7259 | 0.3126 | 0.4548 | 0.6892 | 0.3230 | |
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| 0.1024 | 8.0 | 8296 | 0.3170 | 0.4564 | 0.6868 | 0.3162 | |
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| 0.0939 | 9.0 | 9333 | 0.3280 | 0.4516 | 0.6874 | 0.3110 | |
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| 0.0864 | 10.0 | 10370 | 0.3316 | 0.4501 | 0.6873 | 0.3076 | |
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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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