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nyt_ingredients-crf-tagger-paraphrase-MiniLM-L3-v2

This model is a fine-tuned version of sentence-transformers/paraphrase-MiniLM-L3-v2 on the nyt_ingredients dataset. It achieves the following results on the evaluation set:

  • Loss: 11.3870
  • Comment: {'precision': 0.018842530282637954, 'recall': 0.010174418604651164, 'f1': 0.01321378008494573, 'number': 1376}
  • Name: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1758}
  • Qty: {'precision': 0.1499119991717569, 'recall': 0.9986206896551724, 'f1': 0.26068953101089204, 'number': 1450}
  • Range End: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 14}
  • Unit: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1163}
  • Overall Precision: 0.1405
  • Overall Recall: 0.2538
  • Overall F1: 0.1809
  • Overall Accuracy: 0.1528

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: 32
  • eval_batch_size: 32
  • 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 Comment Name Qty Range End Unit Overall Precision Overall Recall Overall F1 Overall Accuracy
No log 1.0 54 13.0360 {'precision': 0.003246753246753247, 'recall': 0.0007267441860465116, 'f1': 0.001187648456057007, 'number': 1376} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1758} {'precision': 0.142309205350118, 'recall': 0.9979310344827587, 'f1': 0.24909622998794975, 'number': 1450} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 14} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1163} 0.1382 0.2513 0.1784 0.1432
No log 2.0 108 11.3870 {'precision': 0.018842530282637954, 'recall': 0.010174418604651164, 'f1': 0.01321378008494573, 'number': 1376} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1758} {'precision': 0.1499119991717569, 'recall': 0.9986206896551724, 'f1': 0.26068953101089204, 'number': 1450} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 14} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1163} 0.1405 0.2538 0.1809 0.1528

Framework versions

  • Transformers 4.34.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.14.0
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Dataset used to train napsternxg/nyt_ingredients-crf-tagger-paraphrase-MiniLM-L3-v2