Instructions to use Jainam/tinybert-reranker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jainam/tinybert-reranker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jainam/tinybert-reranker")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jainam/tinybert-reranker") model = AutoModelForSequenceClassification.from_pretrained("Jainam/tinybert-reranker", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Jainam/tinybert-reranker: direct link, hf CLI and curl.
- Browser
- Download file 17.6 MB
-
https://huggingface.co/Jainam/tinybert-reranker/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://Jainam/tinybert-reranker@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Jainam/tinybert-reranker/resolve/refs%2Fpr%2F1/pytorch_model.bin
17.6 MB
- Xet hash:
- 4fcefa8c8197b075666aa93840f1203b45636bf7a3d6779ab294a8c676c66690
- Size of remote file:
- 17.6 MB
- SHA256:
- 00f746634d23a76f61df94e114142e90c996e80d760679daf795d8890d660346
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.