metadata
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: uner-bert-ner
results: []
uner-bert-ner
This model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1354
- Precision: 0.8267
- Recall: 0.8707
- F1: 0.8481
- Accuracy: 0.9640
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 144 | 0.1496 | 0.7687 | 0.7971 | 0.7826 | 0.9533 |
| No log | 2.0 | 288 | 0.1429 | 0.7719 | 0.8584 | 0.8129 | 0.9573 |
| No log | 3.0 | 432 | 0.1267 | 0.8014 | 0.8682 | 0.8335 | 0.9629 |
| 0.1628 | 4.0 | 576 | 0.1316 | 0.8206 | 0.8723 | 0.8457 | 0.9644 |
| 0.1628 | 5.0 | 720 | 0.1354 | 0.8267 | 0.8707 | 0.8481 | 0.9640 |
Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.14.5
- Tokenizers 0.13.3