---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
base_model: bert-base-uncased
model-index:
- name: finetuning-sentiment-analysis-en
  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-analysis-en

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0792
- Accuracy: 0.9803
- F1: 0.9856
- Precision: 0.9875
- Recall: 0.9837

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.426         | 1.0   | 1408 | 0.2718          | 0.8910   | 0.9201 | 0.9251    | 0.9151 |
| 0.3247        | 2.0   | 2816 | 0.1552          | 0.9540   | 0.9665 | 0.9656    | 0.9674 |
| 0.1582        | 3.0   | 4224 | 0.0792          | 0.9803   | 0.9856 | 0.9875    | 0.9837 |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1