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: 0.910221531286436
- name: Recall
type: recall
value: 0.9256916996047431
- name: F1
type: f1
value: 0.9178914364099547
- name: Accuracy
type: accuracy
value: 0.98449068822571
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: 0.0635
- Precision: 0.9102
- Recall: 0.9257
- F1: 0.9179
- Accuracy: 0.9845
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 |
|---|---|---|---|---|---|---|---|
| 0.167 | 1.0 | 827 | 0.0505 | 0.9025 | 0.9257 | 0.9140 | 0.9849 |
| 0.0465 | 2.0 | 1654 | 0.0545 | 0.9012 | 0.9300 | 0.9154 | 0.9837 |
| 0.0321 | 3.0 | 2481 | 0.0635 | 0.9102 | 0.9257 | 0.9179 | 0.9845 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1