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COPA_KUCI_xlm_roberta_base_finetuned
Ariffiq99/COPA_KUCI_xlm_roberta_base_finetuned
COPA_KUCI_xlm_roberta_base_finetuned is a multiple choice model from Ariffiq99. Use it for the multiple choice task on the model card, and read the license before you ship it in a product. 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
- multiple choice
- Library
- transformers
- License
- mit
- Model type
- xlm-roberta
- Access
- Public
- Created
- Jun 3, 2024
- Updated
- Jun 3, 2024
- SHA
- e26221f2
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. -->
COPA_KUCI_xlm_roberta_base_finetuned
This model is a fine-tuned version of Ariffiq99/KUCI_xlm_roberta_base_Finetuned on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.0040
- F1: 0.676
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: 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: 7
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| No log | 1.0 | 250 | 1.2356 | 0.65 |
| 0.1809 | 2.0 | 500 | 1.5372 | 0.69 |
| 0.1809 | 3.0 | 750 | 1.9344 | 0.66 |
| 0.0814 | 4.0 | 1000 | 1.9912 | 0.668 |
| 0.0814 | 5.0 | 1250 | 2.1587 | 0.682 |
| 0.2316 | 6.0 | 1500 | 2.0157 | 0.676 |
| 0.2316 | 7.0 | 1750 | 2.0040 | 0.676 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
- Task
- multiple choice
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Jun 3, 2024
- Updated
- Jun 3, 2024