Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use rushikeshwalode/Text_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rushikeshwalode/Text_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rushikeshwalode/Text_Classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rushikeshwalode/Text_Classification") model = AutoModelForSequenceClassification.from_pretrained("rushikeshwalode/Text_Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rushikeshwalode/Text_Classification: direct link, hf CLI and curl.
- Browser
- Download file 5.78 kB
-
https://huggingface.co/rushikeshwalode/Text_Classification/resolve/main/training_args.bin
- Command line
-
hf download hf://rushikeshwalode/Text_Classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rushikeshwalode/Text_Classification/resolve/main/training_args.bin
5.78 kB
- Xet hash:
- 2f10d275e8fbd75cc865eaf46b8988fe3ed653593958f2d66f57b445bc5b55a4
- Size of remote file:
- 5.78 kB
- SHA256:
- 472c1c9fb8278ec091a0212f1399ced2d7bb1f5e9061885c120c1e462d7de6ef
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.