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 pytorch_model.bin from ProceduralTree/final-model: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/ProceduralTree/final-model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ProceduralTree/final-model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ProceduralTree/final-model/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 9730271131cc968ed1e09553fce0fa030fa30be8d755db0a8b6165804c7e8267
- Size of remote file:
- 438 MB
- SHA256:
- 2c27d621a002e218fa4c45e49d291f4907493793689536d93fcbc064f2814a6e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.