Instructions to use CYONG/v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CYONG/v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CYONG/v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CYONG/v1") model = AutoModelForSequenceClassification.from_pretrained("CYONG/v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress epoch 0
Browse files- README.md +3 -3
- tf_model.h5 +1 -1
README.md
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This model is a fine-tuned version of [monologg/koelectra-small-finetuned-sentiment](https://huggingface.co/monologg/koelectra-small-finetuned-sentiment) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 3.
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- Validation Loss: 3.
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- Train Accuracy: 0.5828
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- Epoch: 0
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| Train Loss | Validation Loss | Train Accuracy | Epoch |
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### Framework versions
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This model is a fine-tuned version of [monologg/koelectra-small-finetuned-sentiment](https://huggingface.co/monologg/koelectra-small-finetuned-sentiment) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 3.0772
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- Validation Loss: 3.0391
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- Train Accuracy: 0.5828
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- Epoch: 0
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| Train Loss | Validation Loss | Train Accuracy | Epoch |
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| 3.0772 | 3.0391 | 0.5828 | 0 |
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### Framework versions
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tf_model.h5
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version https://git-lfs.github.com/spec/v1
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size 55349584
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version https://git-lfs.github.com/spec/v1
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oid sha256:0818381501632ae277c505ecfce11293d4a10f91ea63b385f1492e79bb8ccda7
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size 55349584
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