metadata
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: RoBERTa_conll_learning_rate8e5
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9390770252324038
- name: Recall
type: recall
value: 0.9520363513968361
- name: F1
type: f1
value: 0.945512284806953
- name: Accuracy
type: accuracy
value: 0.9877420146666257
RoBERTa_conll_learning_rate8e5
This model is a fine-tuned version of distilroberta-base on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0583
- Precision: 0.9391
- Recall: 0.9520
- F1: 0.9455
- Accuracy: 0.9877
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: 8e-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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.087 | 1.0 | 1756 | 0.0824 | 0.8950 | 0.9212 | 0.9079 | 0.9788 |
| 0.0394 | 2.0 | 3512 | 0.0666 | 0.9396 | 0.9453 | 0.9424 | 0.9859 |
| 0.0206 | 3.0 | 5268 | 0.0583 | 0.9391 | 0.9520 | 0.9455 | 0.9877 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1