--- 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](https://huggingface.co/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