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vp-infoxlm-large-dsc
MiuN2k3/vp-infoxlm-large-dsc
vp-infoxlm-large-dsc is a text classification model from MiuN2k3. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- xlm-roberta
- Access
- Public
- Created
- Sep 14, 2024
- Updated
- Sep 14, 2024
- SHA
- b5c808f6
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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. -->
vp-infoxlm-large-dsc
This model is a fine-tuned version of microsoft/infoxlm-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6113
- Accuracy: 0.8706
- F1: 0.8705
- Precision: 0.8713
- Recall: 0.8706
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: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.8771 | 1.0 | 3180 | 0.8099 | 0.6890 | 0.6914 | 0.7003 | 0.6890 |
| 0.5911 | 2.0 | 6360 | 0.5717 | 0.8014 | 0.8007 | 0.8107 | 0.8014 |
| 0.4608 | 3.0 | 9540 | 0.5323 | 0.8442 | 0.8442 | 0.8449 | 0.8442 |
| 0.407 | 4.0 | 12720 | 0.5047 | 0.8680 | 0.8679 | 0.8683 | 0.8680 |
| 0.3372 | 5.0 | 15900 | 0.6113 | 0.8706 | 0.8705 | 0.8713 | 0.8706 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 3.0.0
- Tokenizers 0.19.1
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- Created
- Sep 14, 2024
- Updated
- Sep 14, 2024