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--- |
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base_model: cardiffnlp/twitter-xlm-roberta-base-sentiment |
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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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model-index: |
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- name: xlm-roberta-meta4types-ft-2.0 |
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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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# xlm-roberta-meta4types-ft-2.0 |
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This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0008 |
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- Roc Auc: 0.6612 |
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- Hamming Loss: 0.2239 |
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- F1 Score: 0.5943 |
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- Accuracy: 0.5392 |
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- Precision: 0.5798 |
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- Recall: 0.6121 |
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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: 5e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Roc Auc | Hamming Loss | F1 Score | Accuracy | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:------------:|:--------:|:--------:|:---------:|:------:| |
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| No log | 1.0 | 204 | 0.5010 | 0.4988 | 0.2042 | 0.2930 | 0.6127 | 0.5948 | 0.3333 | |
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| No log | 2.0 | 408 | 0.5433 | 0.5027 | 0.2010 | 0.3038 | 0.6176 | 0.9281 | 0.3388 | |
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| 0.4958 | 3.0 | 612 | 0.5013 | 0.5043 | 0.2010 | 0.3139 | 0.6127 | 0.8170 | 0.3443 | |
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| 0.4958 | 4.0 | 816 | 0.6563 | 0.6108 | 0.2190 | 0.5211 | 0.5686 | 0.6488 | 0.4799 | |
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| 0.3484 | 5.0 | 1020 | 0.6404 | 0.6444 | 0.1912 | 0.5645 | 0.5980 | 0.6014 | 0.5386 | |
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| 0.3484 | 6.0 | 1224 | 0.9555 | 0.6520 | 0.2614 | 0.5559 | 0.5196 | 0.5889 | 0.5417 | |
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| 0.3484 | 7.0 | 1428 | 0.7919 | 0.6202 | 0.2222 | 0.5417 | 0.5392 | 0.5743 | 0.5297 | |
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| 0.1644 | 8.0 | 1632 | 0.8959 | 0.6389 | 0.2157 | 0.5551 | 0.5539 | 0.5823 | 0.5515 | |
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| 0.1644 | 9.0 | 1836 | 1.0008 | 0.6612 | 0.2239 | 0.5943 | 0.5392 | 0.5798 | 0.6121 | |
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| 0.0611 | 10.0 | 2040 | 0.9594 | 0.6452 | 0.2141 | 0.5822 | 0.5294 | 0.5757 | 0.5893 | |
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### Framework versions |
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- Transformers 4.43.3 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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