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bert-finetuned-large
varunnagda/bert-finetuned-large
bert-finetuned-large is a token classification model from varunnagda. 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
- Jul 27, 2024
- Updated
- Jul 27, 2024
- SHA
- f14baf1e
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Base models
Datasets
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-large
This model is a fine-tuned version of google-bert/bert-large-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0517
- Precision: 0.9433
- Recall: 0.9539
- F1: 0.9485
- Accuracy: 0.9894
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 |
|---|---|---|---|---|---|---|---|
| 0.0639 | 1.0 | 1756 | 0.0610 | 0.9126 | 0.9349 | 0.9236 | 0.9851 |
| 0.0281 | 2.0 | 3512 | 0.0524 | 0.9420 | 0.9504 | 0.9461 | 0.9883 |
| 0.0148 | 3.0 | 5268 | 0.0517 | 0.9433 | 0.9539 | 0.9485 | 0.9894 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Jul 27, 2024
- Updated
- Jul 27, 2024