Instructions to use LinaSaba/distilbert-base-task-multi-label-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LinaSaba/distilbert-base-task-multi-label-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LinaSaba/distilbert-base-task-multi-label-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LinaSaba/distilbert-base-task-multi-label-classification") model = AutoModelForSequenceClassification.from_pretrained("LinaSaba/distilbert-base-task-multi-label-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from LinaSaba/distilbert-base-task-multi-label-classification: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/LinaSaba/distilbert-base-task-multi-label-classification/resolve/refs%2Fpr%2F4/pytorch_model.bin
- Command line
-
hf download hf://LinaSaba/distilbert-base-task-multi-label-classification@refs/pr/4/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/LinaSaba/distilbert-base-task-multi-label-classification/resolve/refs%2Fpr%2F4/pytorch_model.bin
438 MB
- Xet hash:
- bbfe98149ec103998936de7f3546201cc72482381273b56bb65630267bd8dd39
- Size of remote file:
- 438 MB
- SHA256:
- d032b6d8cc795076532dd2baddb2b17784742178d42e6fbeec77d3fcce365279
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.