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WK_NER_RENUM
akshaysayarpro/WK_NER_RENUM
WK_NER_RENUM is a token classification model from akshaysayarpro. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Token Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- May 16, 2024
- Updated
- May 16, 2024
- SHA
- 0dc0db25
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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. -->
WK_NER_RENUM
This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0138
- Precision: 0.9531
- Recall: 0.9692
- F1: 0.9611
- Accuracy: 0.9966
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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0253 | 1.0 | 1080 | 0.0220 | 0.9179 | 0.9355 | 0.9266 | 0.9938 |
| 0.0142 | 2.0 | 2160 | 0.0150 | 0.9444 | 0.9637 | 0.9540 | 0.9959 |
| 0.0072 | 3.0 | 3240 | 0.0138 | 0.9531 | 0.9692 | 0.9611 | 0.9966 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- May 16, 2024
- Updated
- May 16, 2024