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RoBERTa_conll_learning_rate2e5
ICT2214Team7/RoBERTa_conll_learning_rate2e5
RoBERTa_conll_learning_rate2e5 is a token classification model from ICT2214Team7. 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
- roberta
- Access
- Public
- Created
- Jul 3, 2024
- Updated
- Jul 3, 2024
- SHA
- 71007cbe
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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. -->
RoBERTa_conll_learning_rate2e5
This model is a fine-tuned version of distilroberta-base on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0558
- Precision: 0.9394
- Recall: 0.9527
- F1: 0.9460
- Accuracy: 0.9877
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 |
|---|---|---|---|---|---|---|---|
| 0.0747 | 1.0 | 1756 | 0.0614 | 0.9147 | 0.9382 | 0.9263 | 0.9838 |
| 0.0426 | 2.0 | 3512 | 0.0558 | 0.9354 | 0.9498 | 0.9426 | 0.9870 |
| 0.0296 | 3.0 | 5268 | 0.0558 | 0.9394 | 0.9527 | 0.9460 | 0.9877 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
- Task
- Token Classification
- Library
- transformers
- Type
- roberta
- License
- apache-2.0
- Created
- Jul 3, 2024
- Updated
- Jul 3, 2024