tensorboard safetensors generated_from_trainer base_model:bert-base-uncased license:apache-2.0 region:us
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: whatsapp-group-classifierv2
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# whatsapp-group-classifierv2

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4619
- Accuracy: 0.8283
- Precision: 0.8483
- Recall: 0.8347
- F1: 0.8407

## 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: 4e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.9676        | 1.0   | 513  | 0.7151          | 0.7322   | 0.7315    | 0.7148 | 0.7182 |
| 0.6846        | 2.0   | 1026 | 0.5747          | 0.7741   | 0.7918    | 0.7716 | 0.7732 |
| 0.5657        | 3.0   | 1539 | 0.5265          | 0.7946   | 0.8224    | 0.7882 | 0.7983 |
| 0.5294        | 4.0   | 2052 | 0.4870          | 0.8059   | 0.8260    | 0.8084 | 0.8163 |
| 0.4941        | 5.0   | 2565 | 0.4787          | 0.8102   | 0.8326    | 0.8122 | 0.8210 |
| 0.4956        | 6.0   | 3078 | 0.4796          | 0.8156   | 0.8363    | 0.8183 | 0.8252 |
| 0.4701        | 7.0   | 3591 | 0.4812          | 0.8146   | 0.8370    | 0.8165 | 0.8230 |
| 0.4521        | 8.0   | 4104 | 0.4738          | 0.8220   | 0.8398    | 0.8287 | 0.8336 |
| 0.444         | 9.0   | 4617 | 0.4664          | 0.8195   | 0.8412    | 0.8240 | 0.8313 |
| 0.4336        | 10.0  | 5130 | 0.4682          | 0.8244   | 0.8445    | 0.8311 | 0.8370 |
| 0.4345        | 11.0  | 5643 | 0.4653          | 0.8229   | 0.8446    | 0.8285 | 0.8351 |
| 0.4275        | 12.0  | 6156 | 0.4619          | 0.8254   | 0.8463    | 0.8322 | 0.8382 |
| 0.4155        | 13.0  | 6669 | 0.4620          | 0.8288   | 0.8487    | 0.8356 | 0.8414 |
| 0.4179        | 14.0  | 7182 | 0.4624          | 0.8283   | 0.8485    | 0.8354 | 0.8412 |
| 0.4078        | 15.0  | 7695 | 0.4619          | 0.8283   | 0.8483    | 0.8347 | 0.8407 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
