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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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model-index: |
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- name: result |
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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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# result |
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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 the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5662 |
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- Accuracy: 0.8065 |
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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: 16 |
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- eval_batch_size: 16 |
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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: 100 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 0.5234 | 1.0 | 6463 | 0.5311 | 0.7852 | |
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| 0.4135 | 2.0 | 12926 | 0.5020 | 0.8039 | |
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| 0.3246 | 3.0 | 19389 | 0.5662 | 0.8065 | |
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### Testing results |
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precision recall f1-score support |
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0 0.815 0.821 0.818 4449 |
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1 0.752 0.773 0.762 4071 |
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2 0.852 0.823 0.837 4245 |
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accuracy 0.806 12765 |
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macro avg 0.806 0.806 0.806 12765 |
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weighted avg 0.807 0.806 0.807 12765 |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.0.0 |
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- Datasets 2.11.0 |
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- Tokenizers 0.14.1 |
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