---
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