---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: TTC4900Model
  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. -->

# TTC4900Model

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: 0.0667
- Accuracy: 0.9859
- F1: 0.9418
- Precision: 0.9562
- Recall: 0.9309

## 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: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.5192        | 0.3289 | 50   | 0.9342          | 0.7575   | 0.1077 | 0.0947    | 0.125  |
| 0.6007        | 0.6579 | 100  | 0.4256          | 0.8767   | 0.3189 | 0.2983    | 0.3445 |
| 0.2704        | 0.9868 | 150  | 0.2471          | 0.9561   | 0.6877 | 0.6916    | 0.6917 |
| 0.1382        | 1.3158 | 200  | 0.1346          | 0.9727   | 0.8789 | 0.9054    | 0.8698 |
| 0.1132        | 1.6447 | 250  | 0.0824          | 0.9876   | 0.9350 | 0.9701    | 0.9103 |
| 0.0981        | 1.9737 | 300  | 0.0431          | 0.9942   | 0.9749 | 0.9892    | 0.9635 |
| 0.0369        | 2.3026 | 350  | 0.0466          | 0.9892   | 0.9376 | 0.9576    | 0.9275 |
| 0.0373        | 2.6316 | 400  | 0.0413          | 0.9909   | 0.9602 | 0.9580    | 0.9630 |
| 0.0235        | 2.9605 | 450  | 0.0407          | 0.9909   | 0.9613 | 0.9600    | 0.9630 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Tokenizers 0.19.1