metadata
license: apache-2.0
base_model: google-bert/bert-base-multilingual-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: NLP_whole_dataseet_2nd
results: []
NLP_whole_dataseet_2nd
This model is a fine-tuned version of google-bert/bert-base-multilingual-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0646
- Accuracy: 0.9771
- Precision: 0.9747
- Recall: 0.9741
- F1: 0.9738
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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.2952 | 1.0 | 55 | 0.1311 | 0.9725 | 0.9693 | 0.9690 | 0.9691 |
| 0.1988 | 2.0 | 110 | 0.0827 | 0.9679 | 0.9663 | 0.9632 | 0.9638 |
| 0.1823 | 3.0 | 165 | 0.0595 | 0.9771 | 0.9746 | 0.9712 | 0.9724 |
| 0.1237 | 4.0 | 220 | 0.0646 | 0.9771 | 0.9747 | 0.9741 | 0.9738 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1