Instructions to use ielabgroup/Reproduced_In_Batch_Random_Msmarco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ielabgroup/Reproduced_In_Batch_Random_Msmarco with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ielabgroup/Reproduced_In_Batch_Random_Msmarco")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ielabgroup/Reproduced_In_Batch_Random_Msmarco") model = AutoModel.from_pretrained("ielabgroup/Reproduced_In_Batch_Random_Msmarco", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ielabgroup/Reproduced_In_Batch_Random_Msmarco: direct link, hf CLI and curl.
- Browser
- Download file 265 MB
-
https://huggingface.co/ielabgroup/Reproduced_In_Batch_Random_Msmarco/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ielabgroup/Reproduced_In_Batch_Random_Msmarco/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ielabgroup/Reproduced_In_Batch_Random_Msmarco/resolve/main/pytorch_model.bin
265 MB
- Xet hash:
- 834981696ad4d03e4f6edaa1f7451268909b265f5feeb305ec312a08eaa317cf
- Size of remote file:
- 265 MB
- SHA256:
- a24ce93cf53c0b03f667591a14f50682ca45135f45f11af575726a3821bfb9c0
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