---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: finetuning-sentiment
  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

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8125
- Accuracy@en: 0.9033
- F1@en: 0.9002
- Precision@en: 0.8989
- Recall@en: 0.9018
- Loss@en: 0.8125

## 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: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 13

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy@en | F1@en  | Precision@en | Recall@en | Loss@en |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:------:|:------------:|:---------:|:-------:|
| No log        | 1.0   | 375  | 0.4653          | 0.8933      | 0.8895 | 0.8895       | 0.8895    | 0.4653  |
| 0.2086        | 2.0   | 750  | 0.4367          | 0.9033      | 0.9011 | 0.8979       | 0.9069    | 0.4367  |
| 0.1622        | 3.0   | 1125 | 0.4866          | 0.91        | 0.9081 | 0.9047       | 0.9151    | 0.4866  |
| 0.0622        | 4.0   | 1500 | 0.6156          | 0.9         | 0.8982 | 0.8951       | 0.9067    | 0.6156  |
| 0.0622        | 5.0   | 1875 | 0.6790          | 0.9133      | 0.9102 | 0.9102       | 0.9102    | 0.6790  |
| 0.0193        | 6.0   | 2250 | 0.6822          | 0.9         | 0.8978 | 0.8945       | 0.9041    | 0.6822  |
| 0.0202        | 7.0   | 2625 | 0.6595          | 0.91        | 0.9077 | 0.9047       | 0.9126    | 0.6595  |
| 0.0148        | 8.0   | 3000 | 0.6538          | 0.9067      | 0.9042 | 0.9014       | 0.9085    | 0.6538  |
| 0.0148        | 9.0   | 3375 | 0.6869          | 0.9067      | 0.9050 | 0.9018       | 0.9136    | 0.6869  |
| 0.0036        | 10.0  | 3750 | 0.7016          | 0.9033      | 0.9007 | 0.8981       | 0.9044    | 0.7016  |
| 0.0038        | 11.0  | 4125 | 0.8170          | 0.9         | 0.8961 | 0.8972       | 0.8951    | 0.8170  |
| 0.008         | 12.0  | 4500 | 0.8169          | 0.9033      | 0.9002 | 0.8989       | 0.9018    | 0.8169  |
| 0.008         | 13.0  | 4875 | 0.8125          | 0.9033      | 0.9002 | 0.8989       | 0.9018    | 0.8125  |


### Framework versions

- Transformers 4.17.0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2