|
--- |
|
license: apache-2.0 |
|
base_model: bert-base-multilingual-uncased |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- recall |
|
- accuracy |
|
model-index: |
|
- name: multibert_testrun |
|
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. --> |
|
|
|
# multibert_testrun |
|
|
|
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.4300 |
|
- Precisions: 0.8488 |
|
- Recall: 0.7908 |
|
- F-measure: 0.8172 |
|
- Accuracy: 0.9404 |
|
|
|
## 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: 7.5e-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: 14 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:| |
|
| 0.4196 | 1.0 | 269 | 0.3190 | 0.8426 | 0.7090 | 0.7230 | 0.9078 | |
|
| 0.2111 | 2.0 | 538 | 0.2981 | 0.7730 | 0.7491 | 0.7551 | 0.9190 | |
|
| 0.1275 | 3.0 | 807 | 0.2666 | 0.8158 | 0.7744 | 0.7915 | 0.9346 | |
|
| 0.0868 | 4.0 | 1076 | 0.2929 | 0.8276 | 0.7891 | 0.8050 | 0.9349 | |
|
| 0.0608 | 5.0 | 1345 | 0.3253 | 0.8370 | 0.7803 | 0.8043 | 0.9353 | |
|
| 0.0353 | 6.0 | 1614 | 0.3723 | 0.8153 | 0.7999 | 0.8051 | 0.9360 | |
|
| 0.0254 | 7.0 | 1883 | 0.4149 | 0.8266 | 0.7688 | 0.7934 | 0.9339 | |
|
| 0.0203 | 8.0 | 2152 | 0.4399 | 0.8356 | 0.7755 | 0.8028 | 0.9357 | |
|
| 0.0146 | 9.0 | 2421 | 0.4413 | 0.8295 | 0.7845 | 0.8045 | 0.9349 | |
|
| 0.0108 | 10.0 | 2690 | 0.4300 | 0.8488 | 0.7908 | 0.8172 | 0.9404 | |
|
| 0.0054 | 11.0 | 2959 | 0.4428 | 0.8317 | 0.7858 | 0.8062 | 0.9357 | |
|
| 0.004 | 12.0 | 3228 | 0.4681 | 0.8403 | 0.7861 | 0.8095 | 0.9375 | |
|
| 0.0019 | 13.0 | 3497 | 0.4725 | 0.8409 | 0.7901 | 0.8123 | 0.9386 | |
|
| 0.0013 | 14.0 | 3766 | 0.4839 | 0.8437 | 0.7895 | 0.8137 | 0.9404 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.34.0 |
|
- Pytorch 2.0.1+cu118 |
|
- Datasets 2.14.5 |
|
- Tokenizers 0.14.1 |
|
|
