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--- |
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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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- f1 |
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model-index: |
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- name: sentiment-classifier |
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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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# sentiment-classifier |
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This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6947 |
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- Accuracy: 0.4901 |
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- Precision: 0.2402 |
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- Recall: 0.4901 |
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- F1: 0.3224 |
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- F1 Macro: 0.3289 |
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- F1 Negative: 0.0 |
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- Precision Negative: 0.0 |
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- Recall Negative: 0.0 |
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- Support Negative: 900 |
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- F1 Neutral: 0.6578 |
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- Precision Neutral: 0.4901 |
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- Recall Neutral: 1.0 |
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- Support Neutral: 865 |
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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: 256 |
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- eval_batch_size: 256 |
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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: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | F1 Macro | F1 Negative | Precision Negative | Recall Negative | Support Negative | F1 Neutral | Precision Neutral | Recall Neutral | Support Neutral | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:--------:|:-----------:|:------------------:|:---------------:|:----------------:|:----------:|:-----------------:|:--------------:|:---------------:| |
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| 1.1656 | 1.0 | 33 | 0.7228 | 0.5099 | 0.2600 | 0.5099 | 0.3444 | 0.3377 | 0.6754 | 0.5099 | 1.0 | 900 | 0.0 | 0.0 | 0.0 | 865 | |
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| 0.8474 | 2.0 | 66 | 0.7003 | 0.4901 | 0.2402 | 0.4901 | 0.3224 | 0.3289 | 0.0 | 0.0 | 0.0 | 900 | 0.6578 | 0.4901 | 1.0 | 865 | |
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| 0.8033 | 3.0 | 99 | 0.8336 | 0.4901 | 0.2402 | 0.4901 | 0.3224 | 0.3289 | 0.0 | 0.0 | 0.0 | 900 | 0.6578 | 0.4901 | 1.0 | 865 | |
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| 0.7789 | 4.0 | 132 | 0.7006 | 0.5099 | 0.2600 | 0.5099 | 0.3444 | 0.3377 | 0.6754 | 0.5099 | 1.0 | 900 | 0.0 | 0.0 | 0.0 | 865 | |
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| 0.7639 | 5.0 | 165 | 0.6940 | 0.4901 | 0.2402 | 0.4901 | 0.3224 | 0.3289 | 0.0 | 0.0 | 0.0 | 900 | 0.6578 | 0.4901 | 1.0 | 865 | |
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| 0.7385 | 6.0 | 198 | 0.6946 | 0.4901 | 0.2402 | 0.4901 | 0.3224 | 0.3289 | 0.0 | 0.0 | 0.0 | 900 | 0.6578 | 0.4901 | 1.0 | 865 | |
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| 0.7299 | 7.0 | 231 | 0.6961 | 0.4901 | 0.2402 | 0.4901 | 0.3224 | 0.3289 | 0.0 | 0.0 | 0.0 | 900 | 0.6578 | 0.4901 | 1.0 | 865 | |
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| 0.7287 | 8.0 | 264 | 0.6943 | 0.4901 | 0.2402 | 0.4901 | 0.3224 | 0.3289 | 0.0 | 0.0 | 0.0 | 900 | 0.6578 | 0.4901 | 1.0 | 865 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.9.0+cu128 |
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- Datasets 2.18.0 |
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- Tokenizers 0.19.1 |
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