metadata

license: mit
base_model: sagorsarker/bangla-bert-base
tags:
  - generated_from_trainer
metrics:
  - f1
  - accuracy
model-index:
  - name: bangla-bert-base-MLTC-BB2
    results: []

bangla-bert-base-MLTC-BB2

This model is a fine-tuned version of sagorsarker/bangla-bert-base on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.3996
F1: 0.8514
F1 Weighted: 0.8510
Roc Auc: 0.8483
Accuracy: 0.5373
Hamming Loss: 0.1517
Jaccard Score: 0.7412
Zero One Loss: 0.4627

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	F1	F1 Weighted	Roc Auc	Accuracy	Hamming Loss	Jaccard Score	Zero One Loss
0.3931	1.0	73	0.3859	0.8319	0.8312	0.8310	0.5244	0.1690	0.7123	0.4756
0.3635	2.0	146	0.3638	0.8540	0.8546	0.8528	0.5707	0.1472	0.7453	0.4293
0.2688	3.0	219	0.3676	0.8483	0.8465	0.8425	0.5553	0.1575	0.7366	0.4447
0.1841	4.0	292	0.3600	0.8562	0.8558	0.8534	0.5758	0.1465	0.7486	0.4242
0.1701	5.0	365	0.3677	0.8568	0.8565	0.8528	0.5681	0.1472	0.7495	0.4319
0.1499	6.0	438	0.3701	0.8551	0.8541	0.8534	0.5630	0.1465	0.7469	0.4370
0.1034	7.0	511	0.3825	0.8582	0.8576	0.8540	0.5527	0.1459	0.7516	0.4473
0.0793	8.0	584	0.4103	0.8443	0.8428	0.8393	0.5167	0.1607	0.7306	0.4833
0.0801	9.0	657	0.3960	0.8484	0.8484	0.8470	0.5373	0.1530	0.7367	0.4627
0.0812	10.0	730	0.3996	0.8514	0.8510	0.8483	0.5373	0.1517	0.7412	0.4627

Framework versions

Transformers 4.41.2
Pytorch 2.1.2
Datasets 2.19.2
Tokenizers 0.19.1