metadata
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta_large-simple-chunk-conll2003_0819_v0
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: train
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.926378388345934
- name: Recall
type: recall
value: 0.9125794732061762
- name: F1
type: f1
value: 0.9194271595900438
- name: Accuracy
type: accuracy
value: 0.9727360116679926
roberta_large-simple-chunk-conll2003_0819_v0
This model is a fine-tuned version of roberta-large on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0950
- Precision: 0.9264
- Recall: 0.9126
- F1: 0.9194
- Accuracy: 0.9727
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: 1e-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: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.1794 | 1.0 | 878 | 0.0934 | 0.9264 | 0.9217 | 0.9241 | 0.9725 |
| 0.0837 | 2.0 | 1756 | 0.0859 | 0.9351 | 0.9266 | 0.9308 | 0.9749 |
Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1