---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: bert-sst2-sentiment
  results: []
datasets:
- stanfordnlp/sst2
language:
- en
base_model:
- google-bert/bert-base-uncased
pipeline_tag: text-classification
---

<!-- 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. -->

# bert-sst2-sentiment

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2764
- Accuracy: 0.9197
- F1: 0.9197
- Precision: 0.9201
- Recall: 0.9197

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.1654        | 1.0   | 1053 | 0.2157          | 0.9243   | 0.9243 | 0.9247    | 0.9243 |
| 0.099         | 2.0   | 2106 | 0.2533          | 0.9197   | 0.9197 | 0.9197    | 0.9197 |
| 0.0779        | 3.0   | 3159 | 0.2764          | 0.9197   | 0.9197 | 0.9201    | 0.9197 |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.2.1+cu121
- Datasets 3.1.0
- Tokenizers 0.13.3