metadata
license: apache-2.0
base_model: bert-base-multilingual-uncased
tags:
- generated_from_trainer
datasets:
- id_nergrit_corpus
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-multilingual-uncased-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: 0.9022118742724098
- name: Recall
type: recall
value: 0.9189723320158103
- name: F1
type: f1
value: 0.9105149794399845
- name: Accuracy
type: accuracy
value: 0.983813651582688
bert-base-multilingual-uncased-ner-silvanus
This model is a fine-tuned version of bert-base-multilingual-uncased on the id_nergrit_corpus dataset. It achieves the following results on the evaluation set:
- Loss: 0.0662
- Precision: 0.9022
- Recall: 0.9190
- F1: 0.9105
- Accuracy: 0.9838
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 |
|---|---|---|---|---|---|---|---|
| 0.1429 | 1.0 | 827 | 0.0587 | 0.8885 | 0.9075 | 0.8979 | 0.9829 |
| 0.0464 | 2.0 | 1654 | 0.0609 | 0.9081 | 0.9103 | 0.9092 | 0.9846 |
| 0.0288 | 3.0 | 2481 | 0.0662 | 0.9022 | 0.9190 | 0.9105 | 0.9838 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1