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matthewburke_korean_sentiment-finetuned-lora-tweet_eval_irony
TransferGraph/matthewburke_korean_sentiment-finetuned-lora-tweet_eval_irony
matthewburke_korean_sentiment-finetuned-lora-tweet_eval_irony is a text classification model from TransferGraph. Use it when you need a label for a piece of text. It is set up for peft.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- peft
- Access
- Public
- Created
- Feb 27, 2024
- Updated
- Feb 27, 2024
- SHA
- 4ede1ec3
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Base models
Datasets
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. -->
matthewburke_korean_sentiment-finetuned-lora-tweet_eval_irony
This model is a fine-tuned version of matthewburke/korean_sentiment on the tweet_eval dataset. It achieves the following results on the evaluation set:
- accuracy: 0.5906
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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
Training results
| accuracy | train_loss | epoch |
|---|---|---|
| 0.4817 | None | 0 |
| 0.5832 | 0.6875 | 0 |
| 0.5623 | 0.6665 | 1 |
| 0.5675 | 0.6484 | 2 |
| 0.5623 | 0.6406 | 3 |
| 0.5665 | 0.6353 | 4 |
| 0.5592 | 0.6288 | 5 |
| 0.5686 | 0.6241 | 6 |
| 0.5906 | 0.6219 | 7 |
Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.15.2
- Task
- Text Classification
- Library
- peft
- Created
- Feb 27, 2024
- Updated
- Feb 27, 2024