Text Classification
Transformers
Safetensors
bert
lycheemem
memory
reranking
evidence-retrieval
bert-tiny
Instructions to use LycheeMem/reranker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LycheeMem/reranker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LycheeMem/reranker")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LycheeMem/reranker") model = AutoModelForSequenceClassification.from_pretrained("LycheeMem/reranker", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3c6010d24e376e50fe9d74d58b880725478ad07d6d40d3f54ecb184a33a75af4
- Size of remote file:
- 17.5 MB
- SHA256:
- 0a328c53b55cbd49aeec0a44e6b9e2d02d09539e6784d93fc515ba815261fca0
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