---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-trainer
  results: []
language:
- en
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-trainer

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: 0.7809
- Accuracy: 0.8382
- F1: 0.8870

## 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 | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 459  | 0.5148          | 0.7770   | 0.8580 |
| 0.5043        | 2.0   | 918  | 0.5140          | 0.8456   | 0.8927 |
| 0.2697        | 3.0   | 1377 | 0.7809          | 0.8382   | 0.8870 |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Tokenizers 0.21.0

### How to Use

```
from transformers import pipeline

classi = pipeline("text-classification", model="sachin6624/bert-trainer")

sentence1 = "The weather today is sunny and bright."
sentence2 = "It's a bright and sunny day today."

result = classi(f"{sentence1} [SEP] {sentence2}")
print(result)

# LABEL_1' :  if both sentence are similar
# LABEL_0' :  if both sentence are different

```