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fine_tuned_yelp_callback10

Text Classification
Transformers
Safetensors
qwen2
Generated from Trainer
text-generation-inference
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fine_tuned_yelp_callback10 / README.md
Arihant Tripathi
qwen_new_mage_yelp_callback10
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metadata
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2-1.5B
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: fine_tuned_yelp_callback10
    results: []

fine_tuned_yelp_callback10

This model is a fine-tuned version of Qwen/Qwen2-1.5B on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2174
  • Accuracy: 0.9601

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: Use OptimizerNames.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: 3

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.8575 0.0170 100 0.4890 0.8281
0.7118 0.0340 200 0.5564 0.8484
0.5044 0.0509 300 0.5218 0.8463
0.3969 0.0679 400 0.9168 0.7809
0.4494 0.0849 500 0.5158 0.8453
0.3766 0.1019 600 0.5252 0.8772
0.3938 0.1188 700 0.5676 0.8573
0.3681 0.1358 800 0.2930 0.9116
0.3955 0.1528 900 0.2745 0.8845
0.2859 0.1698 1000 0.3442 0.9150
0.329 0.1868 1100 0.2673 0.9116
0.2898 0.2037 1200 0.1957 0.9221
0.2645 0.2207 1300 0.2623 0.9335
0.2906 0.2377 1400 0.2438 0.9280
0.2511 0.2547 1500 0.3281 0.9211
0.3 0.2716 1600 0.1789 0.9467
0.2592 0.2886 1700 0.2963 0.9270
0.2274 0.3056 1800 0.3387 0.9309
0.2843 0.3226 1900 0.2788 0.9291
0.281 0.3396 2000 0.2553 0.9442
0.2624 0.3565 2100 0.1737 0.9547
0.2503 0.3735 2200 0.1948 0.9454
0.2856 0.3905 2300 0.2797 0.9269
0.1531 0.4075 2400 0.2548 0.9490
0.2316 0.4244 2500 0.2906 0.9440
0.2061 0.4414 2600 0.2194 0.9517
0.1991 0.4584 2700 0.1949 0.9515
0.1721 0.4754 2800 0.2730 0.9368
0.1696 0.4924 2900 0.2238 0.9326
0.2013 0.5093 3000 0.1802 0.9603
0.2026 0.5263 3100 0.2174 0.9601

Framework versions

  • Transformers 4.49.0
  • Pytorch 2.6.0+cu126
  • Datasets 3.3.2
  • Tokenizers 0.21.0