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xlnet-large-cased-detect-dep-v2
Trong-Nghia/xlnet-large-cased-detect-dep-v2
xlnet-large-cased-detect-dep-v2 is a text classification model from Trong-Nghia. 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
- xlnet
- Access
- Public
- Created
- Jul 15, 2023
- Updated
- Jul 19, 2023
- SHA
- 5ad27f0b
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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. -->
xlnet-large-cased-detect-dep-v2
This model is a fine-tuned version of xlnet-large-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5807
- Accuracy: 0.708
- F1: 0.7884
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: 5e-06
- 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 | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.6591 | 1.0 | 751 | 0.6108 | 0.661 | 0.7768 |
| 0.6069 | 2.0 | 1502 | 0.5563 | 0.724 | 0.8037 |
| 0.5846 | 3.0 | 2253 | 0.5807 | 0.708 | 0.7884 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- xlnet
- License
- mit
- Created
- Jul 15, 2023
- Updated
- Jul 19, 2023