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_rate6e5
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: 0.9423396477234962
- name: Recall
type: recall
value: 0.9543924604510265
- name: F1
type: f1
value: 0.9483277591973244
- name: Accuracy
type: accuracy
value: 0.9878954312540272
RoBERTa_conll_learning_rate6e5
This model is a fine-tuned version of distilroberta-base on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0563
- Precision: 0.9423
- Recall: 0.9544
- F1: 0.9483
- Accuracy: 0.9879
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: 6e-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 |
|---|---|---|---|---|---|---|---|
| 0.0817 | 1.0 | 1756 | 0.0691 | 0.9078 | 0.9345 | 0.9210 | 0.9824 |
| 0.0366 | 2.0 | 3512 | 0.0639 | 0.9346 | 0.9450 | 0.9397 | 0.9857 |
| 0.0194 | 3.0 | 5268 | 0.0563 | 0.9423 | 0.9544 | 0.9483 | 0.9879 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1