Instructions to use halimara/model_sentence_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use halimara/model_sentence_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="halimara/model_sentence_bert")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("halimara/model_sentence_bert") model = AutoModel.from_pretrained("halimara/model_sentence_bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from halimara/model_sentence_bert: direct link, hf CLI and curl.
- Browser
- Download file 471 MB
-
https://huggingface.co/halimara/model_sentence_bert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://halimara/model_sentence_bert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/halimara/model_sentence_bert/resolve/main/pytorch_model.bin
471 MB
- Xet hash:
- f9bbecd9e555d8063d1a1b3372f0f8de844df85960c0fcd4982ab4687a4c5dc6
- Size of remote file:
- 471 MB
- SHA256:
- e52db37564f2dcbf5c9eac9441dbd0e31b5f74e0d17e2e10ba2d4d419d3c5880
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.