metadata
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta_large-chunk-conll2003_0818_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: 0.9015971148892323
- name: Recall
type: recall
value: 0.9295325779036827
- name: F1
type: f1
value: 0.9153517566036091
- name: Accuracy
type: accuracy
value: 0.978371121718377
roberta_large-chunk-conll2003_0818_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: 0.1566
- Precision: 0.9016
- Recall: 0.9295
- F1: 0.9154
- Accuracy: 0.9784
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 |
|---|---|---|---|---|---|---|---|
| 0.2179 | 1.0 | 878 | 0.0527 | 0.9210 | 0.9472 | 0.9339 | 0.9875 |
| 0.0434 | 2.0 | 1756 | 0.0455 | 0.9366 | 0.9616 | 0.9489 | 0.9899 |
Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1