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 32
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: 3.
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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: 0.0410
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- Validation Loss: 3.2827
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- Epoch: 32
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## Model description
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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 sha256:6bd169ec7c029107600771957b7864f5c8e8a4ded552f00dc52ed6eeb67e4080
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size 539068456
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