---
library_name: transformers
language:
- en
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: bert-base-cased-finetuned-sst2
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE QQP
      type: glue
      args: qqp
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.910784071234232
    - name: F1
      type: f1
      value: 0.8782365054180873
---

<!-- 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-base-cased-finetuned-sst2

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3776
- Accuracy: 0.9108
- F1: 0.8782
- Combined Score: 0.8945

## 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: 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  | Accuracy | Combined Score | F1     | Validation Loss |
|:-------------:|:-----:|:-----:|:--------:|:--------------:|:------:|:---------------:|
| 0.2948        | 1.0   | 22741 | 0.9005   | 0.8834         | 0.8664 | 0.2470          |
| 0.1923        | 2.0   | 45482 | 0.9049   | 0.8884         | 0.8720 | 0.2723          |
| 0.1339        | 3.0   | 68223 | 0.9109   | 0.8954         | 0.8799 | 0.3585          |


### Framework versions

- Transformers 4.45.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1