metadata

base_model: mor40/BulBERT-chitanka-model
tags:
  - generated_from_trainer
datasets:
  - bgglue
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: BulBERT-ner-udep-5epochs
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: bgglue
          type: bgglue
          config: udep
          split: validation
          args: udep
        metrics:
          - name: Precision
            type: precision
            value: 0.975273754856941
          - name: Recall
            type: recall
            value: 0.975273754856941
          - name: F1
            type: f1
            value: 0.975273754856941
          - name: Accuracy
            type: accuracy
            value: 0.9777743637015415

BulBERT-ner-udep-5epochs

This model is a fine-tuned version of mor40/BulBERT-chitanka-model on the bgglue dataset. It achieves the following results on the evaluation set:

Loss: 0.1089
Precision: 0.9753
Recall: 0.9753
F1: 0.9753
Accuracy: 0.9778

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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1134	1.0	1114	0.0996	0.9674	0.9673	0.9673	0.9721
0.0578	2.0	2228	0.0933	0.9728	0.9722	0.9725	0.9760
0.0321	3.0	3342	0.0993	0.9739	0.9746	0.9743	0.9769
0.0178	4.0	4456	0.1054	0.9746	0.9750	0.9748	0.9776
0.0096	5.0	5570	0.1089	0.9753	0.9753	0.9753	0.9778

Framework versions

Transformers 4.34.0
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.14.1