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bert-finetuned-deid-stanford
linbin1973/bert-finetuned-deid-stanford
bert-finetuned-deid-stanford is a token classification model from linbin1973. 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
- bert
- Access
- Public
- Created
- Oct 22, 2024
- Updated
- Oct 22, 2024
- SHA
- 3d2749da
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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. -->
bert-finetuned-deid-stanford
This model is a fine-tuned version of StanfordAIMI/stanford-deidentifier-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0225
- Precision: 0.9652
- Recall: 0.9534
- F1: 0.9593
- Accuracy: 0.9959
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: 32
- eval_batch_size: 32
- 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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 430 | 0.0279 | 0.9276 | 0.9190 | 0.9233 | 0.9940 |
| 0.1053 | 2.0 | 860 | 0.0229 | 0.9590 | 0.9479 | 0.9534 | 0.9958 |
| 0.0179 | 3.0 | 1290 | 0.0225 | 0.9652 | 0.9534 | 0.9593 | 0.9959 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0
- Datasets 2.21.0
- Tokenizers 0.19.1
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Oct 22, 2024
- Updated
- Oct 22, 2024