Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use mohitk24/distilbert_text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mohitk24/distilbert_text with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mohitk24/distilbert_text")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mohitk24/distilbert_text") model = AutoModelForSequenceClassification.from_pretrained("mohitk24/distilbert_text", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mohitk24/distilbert_text: direct link, hf CLI and curl.
- Browser
- Download file 5.78 kB
-
https://huggingface.co/mohitk24/distilbert_text/resolve/main/training_args.bin
- Command line
-
hf download hf://mohitk24/distilbert_text/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mohitk24/distilbert_text/resolve/main/training_args.bin
5.78 kB
- Xet hash:
- fe64fbf4e56afe55310eaf04a5f4bfcaaf398c7a2984d6f3f2c774eece762415
- Size of remote file:
- 5.78 kB
- SHA256:
- df1f6c59ea197fc8df964e88c6c25073eea005741059684c4e9814071fecffd4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.