|
--- |
|
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: [] |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# NLP_whole_dataseet_2nd |
|
|
|
This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/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 |
|
|
