metadata
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-lg-cased-ms-ner-test
results: []
roberta-lg-cased-ms-ner-test
This model is a fine-tuned version of roberta-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1631
- Precision: 0.8047
- Recall: 0.8306
- F1: 0.8174
- Accuracy: 0.9660
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 |
|---|---|---|---|---|---|---|---|
| 0.2027 | 1.0 | 2712 | 0.1739 | 0.7335 | 0.7283 | 0.7309 | 0.9518 |
| 0.1304 | 2.0 | 5424 | 0.1446 | 0.7860 | 0.7674 | 0.7766 | 0.9605 |
| 0.0842 | 3.0 | 8136 | 0.1393 | 0.7892 | 0.8118 | 0.8003 | 0.9629 |
| 0.0556 | 4.0 | 10848 | 0.1498 | 0.8001 | 0.8288 | 0.8142 | 0.9648 |
| 0.0363 | 5.0 | 13560 | 0.1631 | 0.8047 | 0.8306 | 0.8174 | 0.9660 |
Framework versions
- Transformers 4.39.3
- Pytorch 1.12.0
- Datasets 2.18.0
- Tokenizers 0.15.2