metadata
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
base_model: roberta-large
model-index:
- name: roberta-large_ner_conll2003
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- type: precision
value: 0.9622389306599833
name: Precision
- type: recall
value: 0.9692022887916526
name: Recall
- type: f1
value: 0.9657080573488722
name: F1
- type: accuracy
value: 0.9939449398387913
name: Accuracy
roberta-large_ner_conll2003
This model is a fine-tuned version of roberta-large on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0345
- Precision: 0.9622
- Recall: 0.9692
- F1: 0.9657
- Accuracy: 0.9939
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: cosine
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.1227 | 1.0 | 878 | 0.0431 | 0.9511 | 0.9559 | 0.9535 | 0.9914 |
| 0.0295 | 2.0 | 1756 | 0.0334 | 0.9541 | 0.9657 | 0.9599 | 0.9930 |
| 0.0163 | 3.0 | 2634 | 0.0327 | 0.9616 | 0.9682 | 0.9649 | 0.9938 |
| 0.0073 | 4.0 | 3512 | 0.0342 | 0.9624 | 0.9692 | 0.9658 | 0.9939 |
| 0.0042 | 5.0 | 4390 | 0.0345 | 0.9622 | 0.9692 | 0.9657 | 0.9939 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1