| --- |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: sentiment-classifier |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # sentiment-classifier |
|
|
| This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.6947 |
| - Accuracy: 0.4901 |
| - Precision: 0.2402 |
| - Recall: 0.4901 |
| - F1: 0.3224 |
| - F1 Macro: 0.3289 |
| - F1 Negative: 0.0 |
| - Precision Negative: 0.0 |
| - Recall Negative: 0.0 |
| - Support Negative: 900 |
| - F1 Neutral: 0.6578 |
| - Precision Neutral: 0.4901 |
| - Recall Neutral: 1.0 |
| - Support Neutral: 865 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 256 |
| - eval_batch_size: 256 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 10 |
| |
| ### Training results |
| |
| | 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 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:--------:|:-----------:|:------------------:|:---------------:|:----------------:|:----------:|:-----------------:|:--------------:|:---------------:| |
| | 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 | |
| | 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 | |
| | 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 | |
| | 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 | |
| | 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 | |
| | 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 | |
| | 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 | |
| | 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 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.40.2 |
| - Pytorch 2.9.0+cu128 |
| - Datasets 2.18.0 |
| - Tokenizers 0.19.1 |
| |
