metadata
base_model: klue/roberta-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-large-finetuned-ner
results: []
roberta-large-finetuned-ner
This model is a fine-tuned version of klue/roberta-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0290
- Precision: 0.9626
- Recall: 0.9588
- F1: 0.9607
- Accuracy: 0.9936
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: 2
- eval_batch_size: 2
- 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 |
|---|---|---|---|---|---|---|---|
| 0.0682 | 1.0 | 1240 | 0.0561 | 0.9327 | 0.9392 | 0.9359 | 0.9898 |
| 0.0359 | 2.0 | 2480 | 0.0408 | 0.9701 | 0.9539 | 0.9619 | 0.9939 |
| 0.0157 | 3.0 | 3720 | 0.0290 | 0.9626 | 0.9588 | 0.9607 | 0.9936 |
Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.2.dev0
- Tokenizers 0.13.3