---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuning-sentiment-model-distil-finalVersion
  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. -->

# finetuning-sentiment-model-distil-finalVersion

This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6034
- Precision Negative: 0.8333
- Recall Negative: 0.5556
- F1 Negative: 0.6667
- Precision Neutral: 0.75
- Recall Neutral: 0.9
- F1 Neutral: 0.8182
- Precision Positive: 0.8462
- Recall Positive: 0.7857
- F1 Positive: 0.8148
- Accuracy: 0.7907
- Confusion Matrix: [[20, 14, 2], [2, 72, 6], [2, 10, 44]]

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision Negative | Recall Negative | F1 Negative | Precision Neutral | Recall Neutral | F1 Neutral | Precision Positive | Recall Positive | F1 Positive | Accuracy | Confusion Matrix                       |
|:-------------:|:-----:|:----:|:---------------:|:------------------:|:---------------:|:-----------:|:-----------------:|:--------------:|:----------:|:------------------:|:---------------:|:-----------:|:--------:|:--------------------------------------:|
| 0.9478        | 1.0   | 22   | 0.9752          | 0.0                | 0.0             | 0.0         | 0.5064            | 0.9875         | 0.6695     | 0.875              | 0.25            | 0.3889      | 0.5407   | [[0, 35, 1], [0, 79, 1], [0, 42, 14]]  |
| 0.7207        | 2.0   | 44   | 0.6483          | 0.8667             | 0.3611          | 0.5098      | 0.6847            | 0.95           | 0.7958     | 0.8913             | 0.7321          | 0.8039      | 0.7558   | [[13, 21, 2], [1, 76, 3], [1, 14, 41]] |
| 0.4066        | 3.0   | 66   | 0.6153          | 0.7586             | 0.6111          | 0.6769      | 0.7308            | 0.95           | 0.8261     | 1.0                | 0.6964          | 0.8211      | 0.7965   | [[22, 14, 0], [4, 76, 0], [3, 14, 39]] |
| 0.2355        | 4.0   | 88   | 0.6367          | 0.8                | 0.5556          | 0.6557      | 0.7170            | 0.95           | 0.8172     | 0.9756             | 0.7143          | 0.8247      | 0.7907   | [[20, 16, 0], [3, 76, 1], [2, 14, 40]] |
| 0.1048        | 5.0   | 110  | 0.5976          | 0.8333             | 0.5556          | 0.6667      | 0.75              | 0.9            | 0.8182     | 0.8462             | 0.7857          | 0.8148      | 0.7907   | [[20, 14, 2], [2, 72, 6], [2, 10, 44]] |
| 0.0745        | 6.0   | 132  | 0.6034          | 0.8333             | 0.5556          | 0.6667      | 0.75              | 0.9            | 0.8182     | 0.8462             | 0.7857          | 0.8148      | 0.7907   | [[20, 14, 2], [2, 72, 6], [2, 10, 44]] |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3