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bert-ner-custom-v2
nicoheiniger/bert-ner-custom-v2
bert-ner-custom-v2 is a token classification model from nicoheiniger. 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
- Oct 6, 2024
- Updated
- Oct 6, 2024
- SHA
- 1bfc54ec
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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-ner-custom-v2
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1316
- Precision: 0.8231
- Recall: 0.8357
- F1: 0.8294
- Accuracy: 0.9613
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: 8
- 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 |
|---|---|---|---|---|---|---|---|
| 0.1442 | 1.0 | 4796 | 0.1375 | 0.8052 | 0.8311 | 0.8179 | 0.9573 |
| 0.1046 | 2.0 | 9592 | 0.1273 | 0.8260 | 0.8315 | 0.8287 | 0.9606 |
| 0.0834 | 3.0 | 14388 | 0.1316 | 0.8231 | 0.8357 | 0.8294 | 0.9613 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Oct 6, 2024
- Updated
- Oct 6, 2024