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CN_RoBERTa_Dig
vishruthnath/CN_RoBERTa_Dig
CN_RoBERTa_Dig is a text classification model from vishruthnath. Use it when you need a label for a piece of text. 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
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Nov 17, 2023
- Updated
- Nov 18, 2023
- SHA
- ea2eb89f
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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. -->
CN_RoBERTa_Dig
This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0130
- F1: {'f1': 0.9967980788473083}
- Accuracy: {'accuracy': 0.9968}
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: 1e-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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|---|---|---|---|---|---|
| 0.4103 | 0.09 | 1000 | 0.3527 | {'f1': 0.8274601063829787} | {'accuracy': 0.7924} |
| 0.2856 | 0.18 | 2000 | 0.2321 | {'f1': 0.9308926367749896} | {'accuracy': 0.9328} |
| 0.2036 | 0.27 | 3000 | 0.0932 | {'f1': 0.975979268414233} | {'accuracy': 0.9759} |
| 0.1441 | 0.36 | 4000 | 0.0455 | {'f1': 0.9877477836437892} | {'accuracy': 0.9877} |
| 0.0973 | 0.44 | 5000 | 0.0518 | {'f1': 0.9883963106218387} | {'accuracy': 0.9883} |
| 0.0827 | 0.53 | 6000 | 0.0334 | {'f1': 0.9924045572656407} | {'accuracy': 0.9924} |
| 0.0723 | 0.62 | 7000 | 0.0398 | {'f1': 0.9914581449100593} | {'accuracy': 0.9915} |
| 0.0603 | 0.71 | 8000 | 0.0216 | {'f1': 0.9947195377104713} | {'accuracy': 0.9947} |
| 0.044 | 0.8 | 9000 | 0.0285 | {'f1': 0.9932122180075863} | {'accuracy': 0.9932} |
| 0.0476 | 0.89 | 10000 | 0.0138 | {'f1': 0.9964021587047771} | {'accuracy': 0.9964} |
| 0.0392 | 0.98 | 11000 | 0.0130 | {'f1': 0.9967980788473083} | {'accuracy': 0.9968} |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Nov 17, 2023
- Updated
- Nov 18, 2023