Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use sumitbmdi/project_classifier_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sumitbmdi/project_classifier_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sumitbmdi/project_classifier_v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sumitbmdi/project_classifier_v1") model = AutoModelForSequenceClassification.from_pretrained("sumitbmdi/project_classifier_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sumitbmdi/project_classifier_v1: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/sumitbmdi/project_classifier_v1/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://sumitbmdi/project_classifier_v1@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sumitbmdi/project_classifier_v1/resolve/refs%2Fpr%2F1/pytorch_model.bin
268 MB
- Xet hash:
- d716dfeadcaec3cda9bb695efaf9dc7ba6e907cb93bc0532bb9535de8b2d344b
- Size of remote file:
- 268 MB
- SHA256:
- 82b15c9ceb878ba48e39ed2c8f99179b289757dc3608ed4937ceaf37980ad64b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.