metadata
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- id_nergrit_corpus
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-ner-silvanus
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: id_nergrit_corpus
type: id_nergrit_corpus
config: ner
split: validation
args: ner
metrics:
- name: Precision
type: precision
value: 0.9014463504877228
- name: Recall
type: recall
value: 0.9038785834738617
- name: F1
type: f1
value: 0.9026608285618053
- name: Accuracy
type: accuracy
value: 0.9895516717325228
xlm-roberta-base-ner-silvanus
This model is a fine-tuned version of xlm-roberta-base on the id_nergrit_corpus dataset. It achieves the following results on the evaluation set:
- Loss: 0.0457
- Precision: 0.9014
- Recall: 0.9039
- F1: 0.9027
- Accuracy: 0.9896
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: 5e-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 |
|---|---|---|---|---|---|---|---|
| 0.0492 | 1.0 | 1567 | 0.0410 | 0.8863 | 0.8938 | 0.8900 | 0.9886 |
| 0.0285 | 2.0 | 3134 | 0.0416 | 0.8941 | 0.9025 | 0.8983 | 0.9895 |
| 0.0159 | 3.0 | 4701 | 0.0457 | 0.9014 | 0.9039 | 0.9027 | 0.9896 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1