Instructions to use ielabgroup/Reproduced_In_Batch_TAS-B_Msmarco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ielabgroup/Reproduced_In_Batch_TAS-B_Msmarco with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ielabgroup/Reproduced_In_Batch_TAS-B_Msmarco")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ielabgroup/Reproduced_In_Batch_TAS-B_Msmarco") model = AutoModel.from_pretrained("ielabgroup/Reproduced_In_Batch_TAS-B_Msmarco", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ielabgroup/Reproduced_In_Batch_TAS-B_Msmarco: direct link, hf CLI and curl.
- Browser
- Download file 265 MB
-
https://huggingface.co/ielabgroup/Reproduced_In_Batch_TAS-B_Msmarco/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ielabgroup/Reproduced_In_Batch_TAS-B_Msmarco/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ielabgroup/Reproduced_In_Batch_TAS-B_Msmarco/resolve/main/pytorch_model.bin
265 MB
- Xet hash:
- d57e42828226c89d579ce6095acf3609db149d1dfa022ecd68fcebd8cd8a017b
- Size of remote file:
- 265 MB
- SHA256:
- e4086172c5b99280b0aa2365bc684b7a4de2e1fa2b4dbfde739b1adaeed3ef8f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.