Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use ProceduralTree/final-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProceduralTree/final-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ProceduralTree/final-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ProceduralTree/final-model") model = AutoModelForSequenceClassification.from_pretrained("ProceduralTree/final-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ProceduralTree/final-model: direct link, hf CLI and curl.
- Browser
- Download file 3.39 kB
-
https://huggingface.co/ProceduralTree/final-model/resolve/46d07a418ad92514c2fca804e8544e2b1ed8d310/training_args.bin
- Command line
-
hf download hf://ProceduralTree/final-model@46d07a418ad92514c2fca804e8544e2b1ed8d310/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ProceduralTree/final-model/resolve/46d07a418ad92514c2fca804e8544e2b1ed8d310/training_args.bin
3.39 kB
- Xet hash:
- 62698399f2cb635f40bfc75085bc39b7dde5291486f51bbdc6be3c0553e2fc1f
- Size of remote file:
- 3.39 kB
- SHA256:
- 041f1eb7680e640f58e2471b1d589bfbcffd756f1416db3f1bbbcfec54788e98
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