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procit_ner_street_city_names
Deadbody42/procit_ner_street_city_names
procit_ner_street_city_names is a token classification model from Deadbody42. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Token Classification
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Jul 8, 2025
- Updated
- Jul 8, 2025
- SHA
- 189a3363
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Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
procit_ner_street_city_name
This model is a fine-tuned version of roberta-base on an procit006/NER_Street_City_Name_Dataset_Aug27. It achieves the following results on the evaluation set:
- Loss: 0.0075
- Loc: {'precision': 0.9770341725199274, 'recall': 1.0, 'f1-score': 0.9883836972575946, 'support': 12380}
- Per: {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 5590}
- Micro avg: {'precision': 0.9840643995400032, 'recall': 1.0, 'f1-score': 0.9919682040241781, 'support': 17970}
- Macro avg: {'precision': 0.9885170862599637, 'recall': 1.0, 'f1-score': 0.9941918486287973, 'support': 17970}
- Weighted avg: {'precision': 0.9841782446186255, 'recall': 1.0, 'f1-score': 0.9919972271590997, 'support': 17970}
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Loc | Per | Micro avg | Macro avg | Weighted avg |
|---|---|---|---|---|---|---|---|---|
| 0.0918 | 0.2667 | 500 | 0.0146 | {'precision': 0.9762637016008201, 'recall': 1.0, 'f1-score': 0.9879893060931328, 'support': 12380} | {'precision': 0.9969566774078052, 'recall': 0.9962432915921288, 'f1-score': 0.9965998568360772, 'support': 5590} | {'precision': 0.9825915585482017, 'recall': 0.9988313856427379, 'f1-score': 0.9906449209371637, 'support': 17970} | {'precision': 0.9866101895043127, 'recall': 0.9981216457960644, 'f1-score': 0.992294581464605, 'support': 17970} | {'precision': 0.9827007486103386, 'recall': 0.9988313856427379, 'f1-score': 0.9906678246603592, 'support': 17970} |
| 0.0231 | 0.5333 | 1000 | 0.0087 | {'precision': 0.99983429991715, 'recall': 0.9747980613893377, 'f1-score': 0.987157464212679, 'support': 12380} | {'precision': 0.998569896317483, 'recall': 0.9992844364937388, 'f1-score': 0.9989270386266094, 'support': 5590} | {'precision': 0.9994338768115942, 'recall': 0.9824151363383417, 'f1-score': 0.9908514340236854, 'support': 17970} | {'precision': 0.9992020981173165, 'recall': 0.9870412489415382, 'f1-score': 0.9930422514196442, 'support': 17970} | {'precision': 0.9994409768163075, 'recall': 0.9824151363383417, 'f1-score': 0.9908186729480085, 'support': 17970} |
| 0.012 | 0.8 | 1500 | 0.0154 | {'precision': 0.9770341725199274, 'recall': 1.0, 'f1-score': 0.9883836972575946, 'support': 12380} | {'precision': 0.9941186954197113, 'recall': 0.9978533094812164, 'f1-score': 0.9959825015623606, 'support': 5590} | {'precision': 0.9822776501476862, 'recall': 0.9993322203672788, 'f1-score': 0.9907315458457464, 'support': 17970} | {'precision': 0.9855764339698194, 'recall': 0.9989266547406082, 'f1-score': 0.9921830994099776, 'support': 17970} | {'precision': 0.9823487236056142, 'recall': 0.9993322203672788, 'f1-score': 0.9907474878009247, 'support': 17970} |
| 0.0155 | 1.0667 | 2000 | 0.0082 | {'precision': 1.0, 'recall': 0.9747980613893377, 'f1-score': 0.9872382198952879, 'support': 12380} | {'precision': 0.9996420261320924, 'recall': 0.9991055456171736, 'f1-score': 0.9993737138767111, 'support': 5590} | {'precision': 0.9998867176437269, 'recall': 0.9823594880356149, 'f1-score': 0.9910456140350876, 'support': 17970} | {'precision': 0.9998210130660462, 'recall': 0.9869518035032556, 'f1-score': 0.9933059668859996, 'support': 17970} | {'precision': 0.9998886436326321, 'recall': 0.9823594880356149, 'f1-score': 0.9910132566986355, 'support': 17970} |
| 0.0098 | 1.3333 | 2500 | 0.0085 | {'precision': 0.9770305470044992, 'recall': 0.9998384491114701, 'f1-score': 0.9883029262645217, 'support': 12380} | {'precision': 1.0, 'recall': 0.9992844364937388, 'f1-score': 0.9996420901932713, 'support': 5590} | {'precision': 0.9840591618734593, 'recall': 0.9996661101836394, 'f1-score': 0.9918012422360247, 'support': 17970} | {'precision': 0.9885152735022495, 'recall': 0.9995614428026045, 'f1-score': 0.9939725082288965, 'support': 17970} | {'precision': 0.9841757469068281, 'recall': 0.9996661101836394, 'f1-score': 0.9918302454833147, 'support': 17970} |
| 0.0093 | 1.6 | 3000 | 0.0085 | {'precision': 0.9769479750532881, 'recall': 0.9995961227786753, 'f1-score': 0.9881422924901185, 'support': 12380} | {'precision': 0.9991061851984269, 'recall': 0.9998211091234347, 'f1-score': 0.9994635193133047, 'support': 5590} | {'precision': 0.9837358304583539, 'recall': 0.9996661101836394, 'f1-score': 0.9916369959427009, 'support': 17970} | {'precision': 0.9880270801258575, 'recall': 0.999708615951055, 'f1-score': 0.9938029059017116, 'support': 17970} | {'precision': 0.9838408183872519, 'recall': 0.9996661101836394, 'f1-score': 0.991664031941516, 'support': 17970} |
| 0.0109 | 1.8667 | 3500 | 0.0077 | {'precision': 1.0, 'recall': 0.9747980613893377, 'f1-score': 0.9872382198952879, 'support': 12380} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 5590} | {'precision': 1.0, 'recall': 0.9826377295492488, 'f1-score': 0.9912428427079825, 'support': 17970} | {'precision': 1.0, 'recall': 0.9873990306946688, 'f1-score': 0.9936191099476439, 'support': 17970} | {'precision': 1.0, 'recall': 0.9826377295492488, 'f1-score': 0.9912080780358188, 'support': 17970} |
| 0.0067 | 2.1333 | 4000 | 0.0083 | {'precision': 0.9770341725199274, 'recall': 1.0, 'f1-score': 0.9883836972575946, 'support': 12380} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 5590} | {'precision': 0.9840643995400032, 'recall': 1.0, 'f1-score': 0.9919682040241781, 'support': 17970} | {'precision': 0.9885170862599637, 'recall': 1.0, 'f1-score': 0.9941918486287973, 'support': 17970} | {'precision': 0.9841782446186255, 'recall': 1.0, 'f1-score': 0.9919972271590997, 'support': 17970} |
| 0.0075 | 2.4 | 4500 | 0.0076 | {'precision': 1.0, 'recall': 0.9747980613893377, 'f1-score': 0.9872382198952879, 'support': 12380} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 5590} | {'precision': 1.0, 'recall': 0.9826377295492488, 'f1-score': 0.9912428427079825, 'support': 17970} | {'precision': 1.0, 'recall': 0.9873990306946688, 'f1-score': 0.9936191099476439, 'support': 17970} | {'precision': 1.0, 'recall': 0.9826377295492488, 'f1-score': 0.9912080780358188, 'support': 17970} |
| 0.0077 | 2.6667 | 5000 | 0.0076 | {'precision': 0.9770341725199274, 'recall': 1.0, 'f1-score': 0.9883836972575946, 'support': 12380} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 5590} | {'precision': 0.9840643995400032, 'recall': 1.0, 'f1-score': 0.9919682040241781, 'support': 17970} | {'precision': 0.9885170862599637, 'recall': 1.0, 'f1-score': 0.9941918486287973, 'support': 17970} | {'precision': 0.9841782446186255, 'recall': 1.0, 'f1-score': 0.9919972271590997, 'support': 17970} |
| 0.0071 | 2.9333 | 5500 | 0.0075 | {'precision': 0.9770341725199274, 'recall': 1.0, 'f1-score': 0.9883836972575946, 'support': 12380} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 5590} | {'precision': 0.9840643995400032, 'recall': 1.0, 'f1-score': 0.9919682040241781, 'support': 17970} | {'precision': 0.9885170862599637, 'recall': 1.0, 'f1-score': 0.9941918486287973, 'support': 17970} | {'precision': 0.9841782446186255, 'recall': 1.0, 'f1-score': 0.9919972271590997, 'support': 17970} |
Framework versions
- Transformers 4.53.0
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2
- Task
- Token Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Jul 8, 2025
- Updated
- Jul 8, 2025