---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
- text-classification
- transformers
- bert
metrics:
- accuracy
model-index:
- name: bert-finetuned-sst2
  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. -->

# bert-finetuned-sst2

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: 0.3812
- Accuracy: 0.9083

# Load model directly
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("execbat/bert-finetuned-sst2")
model = AutoModelForSequenceClassification.from_pretrained("execbat/bert-finetuned-sst2")
```

## Use a pipeline as a high-level helper
```python
from transformers import pipeline

label_tags = {'LABEL_0' : "NEGATIVE",
             'LABEL_1' : "POSITIVE"}

pipe = pipeline("text-classification", model="execbat/bert-finetuned-sst2")
result = pipe(["what a horrible day!", "what a wonderfull day!"])
encoded_result = [label_tags[i["label"]] for i in result]
print(encoded_result)
```
```python
['NEGATIVE', 'POSITIVE']
```

## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.269         | 1.0   | 8419  | 0.5041          | 0.8716   |
| 0.1854        | 2.0   | 16838 | 0.4296          | 0.8968   |
| 0.0993        | 3.0   | 25257 | 0.3812          | 0.9083   |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0
- Datasets 3.3.2
- Tokenizers 0.21.0