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retriva-bert-preference-classifier

Text Classification
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Safetensors
retrieva-bert
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metadata
license: apache-2.0
base_model: retrieva-jp/bert-1.3b
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: out
    results: []

retriva-bert-preference-classifier

This model is a fine-tuned version of retrieva-jp/bert-1.3b on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4714
  • Accuracy: 0.737
  • Precision: 0.7423
  • Recall: 0.726
  • F1: 0.7341

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: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • training_steps: 1000

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1
0.6438 0.0080 100 0.6116 0.663 0.8721 0.382 0.5313
0.5113 0.0160 200 0.5442 0.699 0.6736 0.772 0.7195
0.4512 0.0240 300 0.5119 0.717 0.8359 0.54 0.6561
0.3916 0.0321 400 0.4936 0.702 0.7295 0.642 0.6830
0.3806 0.0401 500 0.4763 0.715 0.7708 0.612 0.6823
0.3581 0.0481 600 0.4597 0.754 0.75 0.762 0.7560
0.3308 0.0561 700 0.4690 0.742 0.7738 0.684 0.7261
0.3458 0.0641 800 0.4703 0.737 0.7423 0.726 0.7341
0.3475 0.0721 900 0.4728 0.737 0.7495 0.712 0.7303
0.3435 0.0801 1000 0.4714 0.737 0.7423 0.726 0.7341

Evaluation on test split

image/png

Framework versions

  • Transformers 4.43.1
  • Pytorch 2.3.1+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1