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