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scenario-TCR-XLMV_data-cl-cardiff_cl_only_beta
haryoaw/scenario-TCR-XLMV_data-cl-cardiff_cl_only_beta
scenario-TCR-XLMV_data-cl-cardiff_cl_only_beta is a text classification model from haryoaw. 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
- Apr 2, 2024
- Updated
- Apr 2, 2024
- SHA
- 1987bfd1
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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. -->
scenario-TCR-XLMV_data-cl-cardiff_cl_only_beta
This model is a fine-tuned version of facebook/xlm-v-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0986
- Accuracy: 0.3333
- F1: 0.1667
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-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 112233
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.09 | 250 | 1.0993 | 0.3333 | 0.1667 |
| 1.1009 | 2.17 | 500 | 1.0989 | 0.3333 | 0.1667 |
| 1.1009 | 3.26 | 750 | 1.0998 | 0.3333 | 0.1667 |
| 1.0998 | 4.35 | 1000 | 1.0991 | 0.3333 | 0.1667 |
| 1.0998 | 5.43 | 1250 | 1.0987 | 0.3333 | 0.1667 |
| 1.1003 | 6.52 | 1500 | 1.0991 | 0.3333 | 0.1667 |
| 1.1003 | 7.61 | 1750 | 1.0999 | 0.3333 | 0.1667 |
| 1.1001 | 8.7 | 2000 | 1.0988 | 0.3333 | 0.1667 |
| 1.1001 | 9.78 | 2250 | 1.0986 | 0.3333 | 0.1667 |
| 1.0996 | 10.87 | 2500 | 1.0989 | 0.3333 | 0.1667 |
| 1.0996 | 11.96 | 2750 | 1.0989 | 0.3333 | 0.1667 |
| 1.1001 | 13.04 | 3000 | 1.0986 | 0.3333 | 0.1667 |
| 1.1001 | 14.13 | 3250 | 1.0994 | 0.3333 | 0.1667 |
| 1.0981 | 15.22 | 3500 | 1.1405 | 0.3333 | 0.1667 |
| 1.0981 | 16.3 | 3750 | 1.0987 | 0.3333 | 0.1667 |
| 1.0993 | 17.39 | 4000 | 1.0987 | 0.3333 | 0.1667 |
| 1.0993 | 18.48 | 4250 | 1.0990 | 0.3333 | 0.1667 |
| 1.0987 | 19.57 | 4500 | 1.0978 | 0.3457 | 0.2853 |
| 1.0987 | 20.65 | 4750 | 1.0999 | 0.3380 | 0.2148 |
| 1.0943 | 21.74 | 5000 | 1.0987 | 0.3333 | 0.1667 |
| 1.0943 | 22.83 | 5250 | 1.0987 | 0.3333 | 0.1667 |
| 1.0991 | 23.91 | 5500 | 1.0986 | 0.3333 | 0.1667 |
| 1.0991 | 25.0 | 5750 | 1.0986 | 0.3333 | 0.1667 |
| 1.0988 | 26.09 | 6000 | 1.0986 | 0.3333 | 0.1667 |
| 1.0988 | 27.17 | 6250 | 1.0986 | 0.3333 | 0.1667 |
| 1.0991 | 28.26 | 6500 | 1.0986 | 0.3333 | 0.1667 |
| 1.0991 | 29.35 | 6750 | 1.0986 | 0.3333 | 0.1667 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Apr 2, 2024
- Updated
- Apr 2, 2024