metadata
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
datasets:
- id_nergrit_corpus
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-multilingual-cased-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: 0.9068952084144917
- name: Recall
type: recall
value: 0.9201581027667984
- name: F1
type: f1
value: 0.9134785167745734
- name: Accuracy
type: accuracy
value: 0.9851764523984384
bert-base-multilingual-cased-ner-silvanus
This model is a fine-tuned version of bert-base-multilingual-cased on the id_nergrit_corpus dataset. It achieves the following results on the evaluation set:
- Loss: 0.0621
- Precision: 0.9069
- Recall: 0.9202
- F1: 0.9135
- Accuracy: 0.9852
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 |
---|---|---|---|---|---|---|---|
0.1336 | 1.0 | 827 | 0.0551 | 0.9034 | 0.9130 | 0.9082 | 0.9844 |
0.0461 | 2.0 | 1654 | 0.0604 | 0.9098 | 0.9134 | 0.9116 | 0.9842 |
0.0299 | 3.0 | 2481 | 0.0621 | 0.9069 | 0.9202 | 0.9135 | 0.9852 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1