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task1_xlnet-base-cased_3_4_2e-05_0.01
YanJiangJerry/task1_xlnet-base-cased_3_4_2e-05_0.01
task1_xlnet-base-cased_3_4_2e-05_0.01 is a text classification model from YanJiangJerry. 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
- Aug 5, 2023
- Updated
- Aug 5, 2023
- SHA
- cb5b1ee6
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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. -->
task1_xlnet-base-cased_3_4_2e-05_0.01
This model is a fine-tuned version of xlnet-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3630
- Accuracy: 0.9447
- F1: 0.8571
- Precision: 0.8219
- Recall: 0.8955
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: 4
- eval_batch_size: 4
- 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 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.3153 | 1.0 | 1629 | 0.2537 | 0.9336 | 0.8356 | 0.7722 | 0.9104 |
| 0.1964 | 2.0 | 3258 | 0.3178 | 0.9392 | 0.8413 | 0.8140 | 0.8706 |
| 0.0756 | 3.0 | 4887 | 0.3630 | 0.9447 | 0.8571 | 0.8219 | 0.8955 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- xlnet
- License
- mit
- Created
- Aug 5, 2023
- Updated
- Aug 5, 2023