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bert-practice
Lily-Tina/bert-practice
bert-practice is a token classification model from Lily-Tina. 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
- Apr 26, 2024
- Updated
- Apr 26, 2024
- SHA
- fe743ac0
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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-practice
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5468
- Precision: 0.2987
- Recall: 0.4299
- F1: 0.3525
- Accuracy: 0.8237
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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 13 | 0.5920 | 0.2053 | 0.3645 | 0.2626 | 0.8027 |
| No log | 2.0 | 26 | 0.5632 | 0.3007 | 0.4019 | 0.344 | 0.8247 |
| No log | 3.0 | 39 | 0.5468 | 0.2987 | 0.4299 | 0.3525 | 0.8237 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cpu
- Datasets 2.19.0
- Tokenizers 0.19.1
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Apr 26, 2024
- Updated
- Apr 26, 2024