Instructions to use Forturne/instruct_ret_bge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Forturne/instruct_ret_bge with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Forturne/instruct_ret_bge")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Forturne/instruct_ret_bge") model = AutoModel.from_pretrained("Forturne/instruct_ret_bge", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 65aeedce657e0b03c11cd2c0fc8a3d2c8d641c0b2bad0bae89ccd9b3372bc8b6
- Size of remote file:
- 17.1 MB
- SHA256:
- abf1c7593e6054866d04138a9d5c22e9457b3942a92d5fe0e178c0e21868e07d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.