metadata
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-lg-cased-ms-ner-v3-test
results: []
roberta-lg-cased-ms-ner-v3-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: 0.1071
- Precision: 0.8912
- Recall: 0.9039
- F1: 0.8975
- Accuracy: 0.9813
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 |
|---|---|---|---|---|---|---|---|
| 0.1478 | 1.0 | 3615 | 0.1187 | 0.8247 | 0.8225 | 0.8236 | 0.9687 |
| 0.0909 | 2.0 | 7230 | 0.1025 | 0.8617 | 0.8702 | 0.8659 | 0.9753 |
| 0.0552 | 3.0 | 10845 | 0.1016 | 0.8789 | 0.8886 | 0.8837 | 0.9790 |
| 0.0325 | 4.0 | 14460 | 0.0966 | 0.8958 | 0.8956 | 0.8957 | 0.9815 |
| 0.0185 | 5.0 | 18075 | 0.1071 | 0.8912 | 0.9039 | 0.8975 | 0.9813 |
Framework versions
- Transformers 4.39.3
- Pytorch 1.12.0
- Datasets 2.18.0
- Tokenizers 0.15.2