banglat5-bcoqa / README.md
arbitropy's picture
Model save
2184b50 verified
|
raw
history blame
4.65 kB
metadata
base_model: csebuetnlp/banglat5
tags:
  - generated_from_trainer
model-index:
  - name: banglat5-bcoqa
    results: []

banglat5-bcoqa

This model is a fine-tuned version of csebuetnlp/banglat5 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.4135

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: 5e-05
  • train_batch_size: 6
  • eval_batch_size: 6
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 2

Training results

Training Loss Epoch Step Validation Loss
6.2253 0.03 700 2.8997
3.5361 0.06 1400 2.5143
2.8585 0.09 2100 2.3899
2.7763 0.12 2800 2.3435
2.6044 0.15 3500 2.3001
2.6166 0.18 4200 2.2498
2.5002 0.21 4900 2.1958
2.4498 0.24 5600 2.1454
2.4349 0.27 6300 2.1049
2.3176 0.3 7000 2.0382
2.2667 0.33 7700 1.9124
2.2382 0.36 8400 1.7847
2.1296 0.39 9100 1.6963
2.0856 0.42 9800 1.6489
2.0527 0.45 10500 1.6299
2.0363 0.48 11200 1.6085
1.9999 0.51 11900 1.5947
1.9888 0.54 12600 1.5661
1.9438 0.58 13300 1.5666
1.9365 0.61 14000 1.5636
1.9311 0.64 14700 1.5502
1.9649 0.67 15400 1.5419
1.9782 0.7 16100 1.5309
1.8764 0.73 16800 1.5147
1.9236 0.76 17500 1.5066
1.8818 0.79 18200 1.4963
1.9031 0.82 18900 1.4939
1.8583 0.85 19600 1.4923
1.8436 0.88 20300 1.4948
1.8258 0.91 21000 1.4784
1.8701 0.94 21700 1.4642
1.8413 0.97 22400 1.4807
1.8417 1.0 23100 1.4654
1.7898 1.03 23800 1.4711
1.7661 1.06 24500 1.4632
1.7223 1.09 25200 1.4514
1.7461 1.12 25900 1.4568
1.7457 1.15 26600 1.4492
1.7588 1.18 27300 1.4500
1.6475 1.21 28000 1.4515
1.7428 1.24 28700 1.4377
1.782 1.27 29400 1.4456
1.6906 1.3 30100 1.4435
1.6865 1.33 30800 1.4378
1.7806 1.36 31500 1.4327
1.7444 1.39 32200 1.4372
1.7136 1.42 32900 1.4293
1.7252 1.45 33600 1.4246
1.7209 1.48 34300 1.4218
1.7523 1.51 35000 1.4283
1.6808 1.54 35700 1.4216
1.7167 1.57 36400 1.4246
1.7246 1.6 37100 1.4171
1.7614 1.63 37800 1.4204
1.6704 1.66 38500 1.4116
1.6823 1.7 39200 1.4213
1.6744 1.73 39900 1.4236
1.7086 1.76 40600 1.4197
1.7179 1.79 41300 1.4156
1.6223 1.82 42000 1.4205
1.6817 1.85 42700 1.4159
1.6786 1.88 43400 1.4131
1.7163 1.91 44100 1.4147
1.6381 1.94 44800 1.4131
1.6961 1.97 45500 1.4134
1.6247 2.0 46200 1.4135

Framework versions

  • Transformers 4.39.0.dev0
  • Pytorch 2.2.1+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.1