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---
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
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