Instructions to use profoz/deploy-mlops-demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use profoz/deploy-mlops-demo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="profoz/deploy-mlops-demo")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("profoz/deploy-mlops-demo") model = AutoModelForSequenceClassification.from_pretrained("profoz/deploy-mlops-demo") - Notebooks
- Google Colab
- Kaggle
Upload DistilBertForSequenceClassification
Browse files- config.json +1 -1
- pytorch_model.bin +1 -1
config.json
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"sinusoidal_pos_embds": false,
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"tie_weights_": true,
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"torch_dtype": "float32",
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"transformers_version": "4.
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"vocab_size": 30522
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}
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"sinusoidal_pos_embds": false,
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"tie_weights_": true,
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"torch_dtype": "float32",
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"transformers_version": "4.22.1",
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"vocab_size": 30522
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:df2be1215bc41a2bd0720af137729b79da5a0c6cfd740afcc69979f2ae654f9f
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size 267855985
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