---
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: sentiment-bert-base-uncased
  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-bert-base-uncased

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3184
- Precision: 0.8894
- Recall: 0.8897
- F1: 0.8894

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.3301        | 0.9990 | 512  | 0.3780          | 0.8509    | 0.8697 | 0.8514 |
| 0.3103        | 2.0    | 1025 | 0.2916          | 0.8907    | 0.8848 | 0.8870 |
| 0.1607        | 2.9971 | 1536 | 0.3184          | 0.8894    | 0.8897 | 0.8894 |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.15.0
- Tokenizers 0.19.1