Instructions to use KalaiselvanD/model_albert_5000_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/model_albert_5000_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/model_albert_5000_1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/model_albert_5000_1") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/model_albert_5000_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from KalaiselvanD/model_albert_5000_1: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/KalaiselvanD/model_albert_5000_1/resolve/main/training_args.bin
- Command line
-
hf download hf://KalaiselvanD/model_albert_5000_1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/KalaiselvanD/model_albert_5000_1/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- 749ca24a2bbfa4c4c388115f91d8d921ec5eb8e521c151fb1d1be08c22219984
- Size of remote file:
- 4.92 kB
- SHA256:
- 216f5e0cbfb670e20d3f7a0f738bef0ac3d5f2c77e0901e87fc673d062dc2d29
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.