Hugging Face's logo

aiface
/
bert-base-multilingual-uncased-sentiment_v3

Text Classification
Transformers
Safetensors
bert
Generated from Trainer
Model card Files
xet
Community
bert-base-multilingual-uncased-sentiment_v3 / README.md
aiface's picture
aiface
Model save
dda6362 verified
preview code
|
raw
history blame contribute delete
4.23 kB
metadata
library_name: transformers
license: mit
base_model: nlptown/bert-base-multilingual-uncased-sentiment
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: bert-base-multilingual-uncased-sentiment_v3
    results: []

bert-base-multilingual-uncased-sentiment_v3

This model is a fine-tuned version of nlptown/bert-base-multilingual-uncased-sentiment on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4987
  • Accuracy: 0.9286
  • Precision Macro: 0.8226
  • Recall Macro: 0.7931
  • F1 Macro: 0.8061
  • F1 Weighted: 0.9269

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: 64
  • eval_batch_size: 64
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 128
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 20
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Macro Recall Macro F1 Macro F1 Weighted
0.3933 1.0 90 0.2349 0.9292 0.8484 0.7197 0.7474 0.9202
0.2051 2.0 180 0.2166 0.9236 0.8134 0.7619 0.7811 0.9199
0.1494 3.0 270 0.2369 0.9362 0.8619 0.7775 0.8072 0.9321
0.1233 4.0 360 0.2290 0.9343 0.8660 0.7894 0.8176 0.9309
0.0838 5.0 450 0.2490 0.9375 0.8610 0.8200 0.8378 0.9358
0.0799 6.0 540 0.2579 0.9343 0.8528 0.7977 0.8197 0.9317
0.0481 7.0 630 0.3494 0.9223 0.7926 0.8252 0.8064 0.9247
0.0406 8.0 720 0.3154 0.9368 0.8591 0.7986 0.8227 0.9341
0.032 9.0 810 0.3219 0.9305 0.8238 0.8153 0.8194 0.9301
0.0333 10.0 900 0.3787 0.9286 0.8387 0.8048 0.8198 0.9270
0.0278 11.0 990 0.3914 0.9311 0.8432 0.7948 0.8148 0.9288
0.0165 12.0 1080 0.4155 0.9318 0.8627 0.7830 0.8120 0.9282
0.0126 13.0 1170 0.4029 0.9368 0.8550 0.8161 0.8328 0.9352
0.0133 14.0 1260 0.4398 0.9324 0.8460 0.7915 0.8134 0.9297
0.01 15.0 1350 0.4571 0.9318 0.8347 0.7913 0.8094 0.9294
0.008 16.0 1440 0.4685 0.9299 0.8303 0.7899 0.8070 0.9276
0.0058 17.0 1530 0.4846 0.9318 0.8403 0.7954 0.8142 0.9295
0.0022 18.0 1620 0.4905 0.9280 0.8249 0.7928 0.8068 0.9262
0.0038 19.0 1710 0.5043 0.9299 0.8272 0.7897 0.8057 0.9277
0.0015 20.0 1800 0.4987 0.9286 0.8226 0.7931 0.8061 0.9269

Framework versions

  • Transformers 4.55.0
  • Pytorch 2.7.0+cu126
  • Datasets 4.0.0
  • Tokenizers 0.21.4