metadata
license: mit
base_model: camembert-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: RoBERTa-ner
results: []
RoBERTa-ner
This model is a fine-tuned version of camembert-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0393
- Precision: 0.9106
- Recall: 0.9165
- F1: 0.9136
- Accuracy: 0.9881
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
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0503 | 1.0 | 5867 | 0.0463 | 0.9036 | 0.9078 | 0.9057 | 0.9866 |
| 0.036 | 2.0 | 11734 | 0.0410 | 0.9126 | 0.9156 | 0.9141 | 0.9876 |
| 0.0254 | 3.0 | 17601 | 0.0413 | 0.9150 | 0.9185 | 0.9168 | 0.9881 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2
- Datasets 2.19.0
- Tokenizers 0.19.1