metadata
base_model: klue/roberta-small
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: klue_roberta_small_ner_identified
results: []
klue_roberta_small_ner_identified
This model is a fine-tuned version of klue/roberta-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0212
- Precision: 0.9803
- Recall: 1.0
- F1: 0.9901
- Accuracy: 0.9980
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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 15 | 0.2866 | 0.1199 | 0.2739 | 0.1668 | 0.9287 |
| No log | 2.0 | 30 | 0.1369 | 0.6599 | 0.7996 | 0.7231 | 0.9654 |
| No log | 3.0 | 45 | 0.0629 | 0.8088 | 0.9042 | 0.8538 | 0.9915 |
| No log | 4.0 | 60 | 0.0381 | 0.9760 | 0.9978 | 0.9868 | 0.9969 |
| No log | 5.0 | 75 | 0.0276 | 0.9781 | 0.9955 | 0.9868 | 0.9981 |
| No log | 6.0 | 90 | 0.0238 | 0.9803 | 1.0 | 0.9901 | 0.9979 |
| No log | 7.0 | 105 | 0.0224 | 0.9803 | 1.0 | 0.9901 | 0.9979 |
| No log | 8.0 | 120 | 0.0212 | 0.9803 | 1.0 | 0.9901 | 0.9980 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1