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