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KUCI_COPA_Bert_Base_Uncased_Finetuned
Ariffiq99/KUCI_COPA_Bert_Base_Uncased_Finetuned
KUCI_COPA_Bert_Base_Uncased_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 apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- multiple choice
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Jun 25, 2024
- Updated
- Jun 25, 2024
- SHA
- da3f0a0a
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. -->
KUCI_COPA_Bert_Base_Uncased_Finetuned
This model is a fine-tuned version of Ariffiq99/COPA_Bert_Base_Uncased_Finetuned on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.1247
- F1: 0.5208
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 |
|---|---|---|---|---|
| 1.2021 | 1.0 | 5196 | 1.1920 | 0.4673 |
| 1.1512 | 2.0 | 10392 | 1.1492 | 0.4987 |
| 1.1004 | 3.0 | 15588 | 1.1244 | 0.5088 |
| 1.0617 | 4.0 | 20784 | 1.1205 | 0.5191 |
| 1.025 | 5.0 | 25980 | 1.1247 | 0.5208 |
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
- bert
- License
- apache-2.0
- Created
- Jun 25, 2024
- Updated
- Jun 25, 2024