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")# 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
- Xet hash:
- 4ebf507977f677627809a5d220ec9560fa81acd0051bc21259cf7128e7567ea5
- Size of remote file:
- 265 MB
- SHA256:
- 222dae1253520fb1fce93c4eab7e20a8dcf3089f262264ec0bb9224568e2b2df
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