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proximity_cs_model_with_test
levshechter/proximity_cs_model_with_test
proximity_cs_model_with_test is a token classification model from levshechter. Use it when you need labels on individual words, such as names. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Token Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Sep 16, 2025
- Updated
- Sep 16, 2025
- SHA
- 8394794f
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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. -->
proximity_cs_model_with_test
This model is a fine-tuned version of OMRIDRORI/mbert-tibetan-continual-unicode-240k on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3626
- Accuracy: 0.9896
- Proximity F1: 0.2190
- Proximity Recall: 0.2370
- Proximity Precision: 0.2504
- Exact Matches: 0.6923
- Missed Switches: 0.5385
- False Switches: 2.4872
- Matches At 1 Words: 0.0
- Matches At 2 Words: 0.0
- Matches At 3 Words: 0.0
- Matches At 4 Words: 0.0
- Matches At 5 Words: 0.0
- Matches At 6 Words: 0.0
- Matches At 7 Words: 0.0
- Matches At 8 Words: 0.0
- Matches At 9 Words: 0.0
- Matches At 10 Words: 0.0
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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 25
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Proximity F1 | Proximity Recall | Proximity Precision | Exact Matches | Missed Switches | False Switches | Matches At 1 Words | Matches At 2 Words | Matches At 3 Words | Matches At 4 Words | Matches At 5 Words | Matches At 6 Words | Matches At 7 Words | Matches At 8 Words | Matches At 9 Words | Matches At 10 Words |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.3554 | 7.6923 | 100 | 0.3063 | 0.9314 | 0.0892 | 0.3119 | 0.0543 | 1.0 | 0.0513 | 19.7179 | 0.0513 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0256 | 0.0 | 0.0 | 0.0256 | 0.0769 |
| 0.0729 | 15.3846 | 200 | 0.2531 | 0.9791 | 0.1595 | 0.2241 | 0.1791 | 0.6923 | 0.4872 | 5.5128 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0513 | 0.0 |
| 0.0156 | 23.0769 | 300 | 0.3626 | 0.9896 | 0.2190 | 0.2370 | 0.2504 | 0.6923 | 0.5385 | 2.4872 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- Created
- Sep 16, 2025
- Updated
- Sep 16, 2025