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e_care_COPA_xlm_roberta_large_finetuned
Ariffiq99/e_care_COPA_xlm_roberta_large_finetuned
e_care_COPA_xlm_roberta_large_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 23, 2024
- Updated
- Jun 23, 2024
- SHA
- 422beb09
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. -->
e_care_COPA_xlm_roberta_large_finetuned
This model is a fine-tuned version of Ariffiq99/COPA_xlm_roberta_base_finetuned on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5818
- F1: 0.6744
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.6938 | 1.0 | 933 | 0.6374 | 0.5999 |
| 0.6404 | 2.0 | 1866 | 0.6066 | 0.6329 |
| 0.62 | 3.0 | 2799 | 0.5958 | 0.6550 |
| 0.5887 | 4.0 | 3732 | 0.5867 | 0.6659 |
| 0.5705 | 5.0 | 4665 | 0.5818 | 0.6744 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Task
- multiple choice
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Jun 23, 2024
- Updated
- Jun 23, 2024