Feature Extraction
Transformers
PyTorch
English
t5
contrastive learning
ranking
decoding
metric learning
text generation
retrieval
custom_code
Instructions to use kalpeshk2011/rankgen-t5-base-all with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kalpeshk2011/rankgen-t5-base-all with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="kalpeshk2011/rankgen-t5-base-all", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("kalpeshk2011/rankgen-t5-base-all", trust_remote_code=True) model = AutoModel.from_pretrained("kalpeshk2011/rankgen-t5-base-all", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update model metadata to set pipeline tag to the new `text-ranking`
#2 opened over 1 year ago
by
tomaarsen
Adding `safetensors` variant of this model
#1 opened over 1 year ago
by
SFconvertbot