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tiny_bert_25_intents

Text Classification
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PyTorch
bert
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tiny_bert_25_intents / README.md
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metadata
license: mit
base_model: prajjwal1/bert-tiny
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
model-index:
  - name: tiny_bert_25_intents
    results: []

tiny_bert_25_intents

This model is a fine-tuned version of prajjwal1/bert-tiny on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2718
  • Accuracy: 0.9238
  • F1: 0.9238

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: 0.0001
  • 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: 15

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
No log 1.0 472 1.9552 0.6143 0.6143
2.6425 2.0 944 1.1750 0.7952 0.7952
1.5608 3.0 1416 0.7802 0.8667 0.8667
0.9473 4.0 1888 0.5623 0.8976 0.8976
0.6217 5.0 2360 0.4360 0.9167 0.9167
0.4313 6.0 2832 0.3584 0.9143 0.9143
0.3165 7.0 3304 0.3150 0.9238 0.9238
0.237 8.0 3776 0.3006 0.9214 0.9214
0.1674 9.0 4248 0.2799 0.9214 0.9214
0.1691 10.0 4720 0.2647 0.9238 0.9238
0.1263 11.0 5192 0.2742 0.9238 0.9238
0.1201 12.0 5664 0.2745 0.9214 0.9214
0.1132 13.0 6136 0.2641 0.9214 0.9214
0.1045 14.0 6608 0.2728 0.9238 0.9238
0.0918 15.0 7080 0.2718 0.9238 0.9238

Framework versions

  • Transformers 4.33.1
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.13.3