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 9
Browse files- README.md +4 -3
- tf_model.h5 +1 -1
README.md
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This model was trained from scratch on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.
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- Validation Loss: 2.
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- Epoch:
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## Model description
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### Framework versions
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This model was trained from scratch on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.1582
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- Validation Loss: 2.4016
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- Epoch: 9
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## Model description
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| 0.2640 | 2.2600 | 6 |
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| 0.2151 | 2.2474 | 7 |
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| 0.1811 | 2.3162 | 8 |
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| 0.1582 | 2.4016 | 9 |
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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 539068456
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version https://git-lfs.github.com/spec/v1
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oid sha256:fe05290f1e34b6b20ae9274ef1ed597c57dbee9f8fe0273cc17f36b4cf9e1c31
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size 539068456
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