Instructions to use KalaiselvanD/model_albert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/model_albert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/model_albert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/model_albert") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/model_albert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from KalaiselvanD/model_albert: direct link, hf CLI and curl.
- Browser
- Download file 46.7 MB
-
https://huggingface.co/KalaiselvanD/model_albert/resolve/main/model.safetensors
- Command line
-
hf download hf://KalaiselvanD/model_albert/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/KalaiselvanD/model_albert/resolve/main/model.safetensors
46.7 MB
- Xet hash:
- dbd44483d50e20fc696ae269c55b2239be9441ad0de75bb53271c9b80797b489
- Size of remote file:
- 46.7 MB
- SHA256:
- 252594ae6a95e0d668df600a9759eb5c5ab05576427b01bcce4c8ea19842158d
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