Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use krishnareddy/hello_classification_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use krishnareddy/hello_classification_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="krishnareddy/hello_classification_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("krishnareddy/hello_classification_model") model = AutoModelForSequenceClassification.from_pretrained("krishnareddy/hello_classification_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from krishnareddy/hello_classification_model: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/krishnareddy/hello_classification_model/resolve/main/model.safetensors
- Command line
-
hf download hf://krishnareddy/hello_classification_model/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/krishnareddy/hello_classification_model/resolve/main/model.safetensors
268 MB
- Xet hash:
- 784b021a5d593b988c872f2d4f32f80aa1dd30bc055591ce09d159db02e2c739
- Size of remote file:
- 268 MB
- SHA256:
- 9fe3da2197ee3a23e5bffb8cfc6ba28fdc9aff4e5777f2b668ed4a0841a934d7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.