metadata
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
datasets:
- wikiann
model-index:
- name: fine_tuned_BERT_cs_wikann
results: []
fine_tuned_BERT_cs_wikann
This model is a fine-tuned version of bert-base-multilingual-cased on the wikiann dataset. It achieves the following results on the evaluation set:
- Loss: 0.1428
- Overall Precision: 0.9090
- Overall Recall: 0.9274
- Overall F1: 0.9181
- Overall Accuracy: 0.9673
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: 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: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|
| 0.3011 | 0.4 | 500 | 0.1781 | 0.8588 | 0.8721 | 0.8654 | 0.9501 |
| 0.1717 | 0.8 | 1000 | 0.1524 | 0.8733 | 0.9033 | 0.8880 | 0.9565 |
| 0.1307 | 1.2 | 1500 | 0.1443 | 0.9058 | 0.9051 | 0.9054 | 0.9639 |
| 0.0968 | 1.6 | 2000 | 0.1392 | 0.9075 | 0.9107 | 0.9091 | 0.9651 |
| 0.0974 | 2.0 | 2500 | 0.1352 | 0.9030 | 0.9201 | 0.9115 | 0.9647 |
| 0.0603 | 2.4 | 3000 | 0.1410 | 0.9091 | 0.9217 | 0.9154 | 0.9667 |
| 0.054 | 2.8 | 3500 | 0.1428 | 0.9090 | 0.9274 | 0.9181 | 0.9673 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.15.0