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

# newsdata-bert

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4835
- Accuracy: 0.8617
- Precision: 0.8494
- F1: 0.8533
- Recall: 0.8617

## 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: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | F1     | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.2095        | 0.1024 | 1000 | 1.0182          | 0.7335   | 0.6811    | 0.6915 | 0.7335 |
| 0.8995        | 0.2048 | 2000 | 0.8102          | 0.7798   | 0.7622    | 0.7586 | 0.7798 |
| 0.7554        | 0.3071 | 3000 | 0.6720          | 0.8165   | 0.7938    | 0.8023 | 0.8165 |
| 0.6805        | 0.4095 | 4000 | 0.6185          | 0.828    | 0.8107    | 0.8157 | 0.828  |
| 0.6192        | 0.5119 | 5000 | 0.5865          | 0.8322   | 0.8233    | 0.8226 | 0.8322 |
| 0.5963        | 0.6143 | 6000 | 0.5462          | 0.8475   | 0.8333    | 0.8356 | 0.8475 |
| 0.5466        | 0.7166 | 7000 | 0.5384          | 0.849    | 0.8386    | 0.8398 | 0.849  |
| 0.5447        | 0.8190 | 8000 | 0.4923          | 0.8582   | 0.8440    | 0.8491 | 0.8582 |
| 0.5288        | 0.9214 | 9000 | 0.4835          | 0.8617   | 0.8494    | 0.8533 | 0.8617 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1