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16class_800_newtest_xlm_robt_24nov23_v1
dsmsb/16class_800_newtest_xlm_robt_24nov23_v1
16class_800_newtest_xlm_robt_24nov23_v1 is a text classification model from dsmsb. 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
- xlm-roberta
- Access
- Public
- Created
- Nov 24, 2023
- Updated
- Nov 24, 2023
- SHA
- c82f13ff
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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. -->
16class_800_newtest_xlm_robt_24nov23_v1
This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1151
- Accuracy: 0.9774
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: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.8283 | 1.0 | 1529 | 0.6162 | 0.8168 |
| 0.5779 | 2.0 | 3058 | 0.3744 | 0.8873 |
| 0.4409 | 3.0 | 4587 | 0.2805 | 0.9229 |
| 0.359 | 4.0 | 6116 | 0.2288 | 0.9434 |
| 0.2794 | 5.0 | 7645 | 0.1791 | 0.9598 |
| 0.2564 | 6.0 | 9174 | 0.1564 | 0.9668 |
| 0.198 | 7.0 | 10703 | 0.1274 | 0.9733 |
| 0.1773 | 8.0 | 12232 | 0.1151 | 0.9774 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Nov 24, 2023
- Updated
- Nov 24, 2023