metadata
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
base_model: roberta-large
model-index:
- name: roberta-large-finetuned-ner
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- type: precision
value: 0.9476811355009077
name: Precision
- type: recall
value: 0.9663412992258499
name: Recall
- type: f1
value: 0.9569202566452795
name: F1
- type: accuracy
value: 0.990656929827253
name: Accuracy
roberta-large-finetuned-ner
This model is a fine-tuned version of roberta-large on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0495
- Precision: 0.9477
- Recall: 0.9663
- F1: 0.9569
- Accuracy: 0.9907
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: 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 |
|---|---|---|---|---|---|---|---|
| 0.078 | 1.0 | 1756 | 0.0577 | 0.9246 | 0.9536 | 0.9389 | 0.9865 |
| 0.0382 | 2.0 | 3512 | 0.0528 | 0.9414 | 0.9620 | 0.9516 | 0.9890 |
| 0.021 | 3.0 | 5268 | 0.0495 | 0.9477 | 0.9663 | 0.9569 | 0.9907 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1