README.md

metadata

base_model: lilyyellow/my_awesome_ner-token_classification_v1.0.7-5
tags:
  - generated_from_trainer
model-index:
  - name: my_awesome_ner-token_classification_v1.0.7-5
    results: []

my_awesome_ner-token_classification_v1.0.7-5

This model is a fine-tuned version of lilyyellow/my_awesome_ner-token_classification_v1.0.7-5 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.5208
• Age: {'precision': 0.8493150684931506, 'recall': 0.9393939393939394, 'f1': 0.8920863309352518, 'number': 132}
• Datetime: {'precision': 0.7049180327868853, 'recall': 0.7428861788617886, 'f1': 0.723404255319149, 'number': 984}
• Disease: {'precision': 0.6953405017921147, 'recall': 0.6855123674911661, 'f1': 0.6903914590747331, 'number': 283}
• Event: {'precision': 0.30033003300330036, 'recall': 0.3446969696969697, 'f1': 0.3209876543209877, 'number': 264}
• Gender: {'precision': 0.7647058823529411, 'recall': 0.7982456140350878, 'f1': 0.7811158798283262, 'number': 114}
• Law: {'precision': 0.5303514376996805, 'recall': 0.6561264822134387, 'f1': 0.5865724381625441, 'number': 253}
• Location: {'precision': 0.7111228255139694, 'recall': 0.7375615090213231, 'f1': 0.7241009125067096, 'number': 1829}
• Organization: {'precision': 0.6420640104506858, 'recall': 0.6991465149359887, 'f1': 0.6693905345590739, 'number': 1406}
• Person: {'precision': 0.6987087517934003, 'recall': 0.7295880149812735, 'f1': 0.7138145840967388, 'number': 1335}
• Phone: {'precision': 0.8522727272727273, 'recall': 0.9615384615384616, 'f1': 0.9036144578313254, 'number': 78}
• Product: {'precision': 0.4, 'recall': 0.3828125, 'f1': 0.3912175648702595, 'number': 256}
• Quantity: {'precision': 0.5313001605136437, 'recall': 0.6084558823529411, 'f1': 0.567266495287061, 'number': 544}
• Role: {'precision': 0.4302721088435374, 'recall': 0.48747591522157996, 'f1': 0.45709123757904246, 'number': 519}
• Transportation: {'precision': 0.5, 'recall': 0.6231884057971014, 'f1': 0.5548387096774193, 'number': 138}
• Overall Precision: 0.6349
• Overall Recall: 0.6817
• Overall F1: 0.6575
• Overall Accuracy: 0.8878

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: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Age	Datetime	Disease	Event	Gender	Law	Location	Organization	Person	Phone	Product	Quantity	Role	Transportation	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.1172	1.9991	2313	0.4711	{'precision': 0.8620689655172413, 'recall': 0.946969696969697, 'f1': 0.9025270758122743, 'number': 132}	{'precision': 0.6928909952606636, 'recall': 0.7428861788617886, 'f1': 0.7170181461500736, 'number': 984}	{'precision': 0.7089552238805971, 'recall': 0.6713780918727915, 'f1': 0.689655172413793, 'number': 283}	{'precision': 0.3090909090909091, 'recall': 0.32196969696969696, 'f1': 0.3153988868274582, 'number': 264}	{'precision': 0.7520661157024794, 'recall': 0.7982456140350878, 'f1': 0.7744680851063831, 'number': 114}	{'precision': 0.5795053003533569, 'recall': 0.6482213438735178, 'f1': 0.6119402985074627, 'number': 253}	{'precision': 0.7174721189591078, 'recall': 0.7386550027337343, 'f1': 0.7279094827586208, 'number': 1829}	{'precision': 0.6510554089709762, 'recall': 0.7019914651493598, 'f1': 0.675564681724846, 'number': 1406}	{'precision': 0.720666161998486, 'recall': 0.7131086142322097, 'f1': 0.716867469879518, 'number': 1335}	{'precision': 0.7816091954022989, 'recall': 0.8717948717948718, 'f1': 0.8242424242424243, 'number': 78}	{'precision': 0.38288288288288286, 'recall': 0.33203125, 'f1': 0.35564853556485354, 'number': 256}	{'precision': 0.5684575389948007, 'recall': 0.6029411764705882, 'f1': 0.5851917930419268, 'number': 544}	{'precision': 0.4645390070921986, 'recall': 0.5048169556840078, 'f1': 0.4838411819021238, 'number': 519}	{'precision': 0.47368421052631576, 'recall': 0.5869565217391305, 'f1': 0.5242718446601942, 'number': 138}	0.6480	0.6761	0.6617	0.8890
0.0813	3.9983	4626	0.5208	{'precision': 0.8493150684931506, 'recall': 0.9393939393939394, 'f1': 0.8920863309352518, 'number': 132}	{'precision': 0.7049180327868853, 'recall': 0.7428861788617886, 'f1': 0.723404255319149, 'number': 984}	{'precision': 0.6953405017921147, 'recall': 0.6855123674911661, 'f1': 0.6903914590747331, 'number': 283}	{'precision': 0.30033003300330036, 'recall': 0.3446969696969697, 'f1': 0.3209876543209877, 'number': 264}	{'precision': 0.7647058823529411, 'recall': 0.7982456140350878, 'f1': 0.7811158798283262, 'number': 114}	{'precision': 0.5303514376996805, 'recall': 0.6561264822134387, 'f1': 0.5865724381625441, 'number': 253}	{'precision': 0.7111228255139694, 'recall': 0.7375615090213231, 'f1': 0.7241009125067096, 'number': 1829}	{'precision': 0.6420640104506858, 'recall': 0.6991465149359887, 'f1': 0.6693905345590739, 'number': 1406}	{'precision': 0.6987087517934003, 'recall': 0.7295880149812735, 'f1': 0.7138145840967388, 'number': 1335}	{'precision': 0.8522727272727273, 'recall': 0.9615384615384616, 'f1': 0.9036144578313254, 'number': 78}	{'precision': 0.4, 'recall': 0.3828125, 'f1': 0.3912175648702595, 'number': 256}	{'precision': 0.5313001605136437, 'recall': 0.6084558823529411, 'f1': 0.567266495287061, 'number': 544}	{'precision': 0.4302721088435374, 'recall': 0.48747591522157996, 'f1': 0.45709123757904246, 'number': 519}	{'precision': 0.5, 'recall': 0.6231884057971014, 'f1': 0.5548387096774193, 'number': 138}	0.6349	0.6817	0.6575	0.8878

Framework versions

• Transformers 4.41.2
• Pytorch 2.1.2
• Datasets 2.19.2
• Tokenizers 0.19.1