Feature Extraction
Transformers
Safetensors
bert
tevatron
tevatron-elastic
information-retrieval
reranker
elastic
text-embeddings-inference
Instructions to use utahnlp/tevatron-elastic-bert-reranker-depth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use utahnlp/tevatron-elastic-bert-reranker-depth with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="utahnlp/tevatron-elastic-bert-reranker-depth")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("utahnlp/tevatron-elastic-bert-reranker-depth") model = AutoModel.from_pretrained("utahnlp/tevatron-elastic-bert-reranker-depth") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
base_model: google-bert/bert-base-uncased
library_name: transformers
tags:
- tevatron
- tevatron-elastic
- information-retrieval
- reranker
- elastic
tevatron-elastic-bert-reranker-depth
A reranker trained with Tevatron-Elastic, which trains one checkpoint to serve many operating points along the depth / width / token compression axes. This checkpoint is an elastic depth axis (early exit): one checkpoint serves several layer counts.
- Base model:
google-bert/bert-base-uncased - Task: reranker
- Elastic axis: depth
- Training data: rlhn/rlhn-680K, max length 512,
query:/passage:prefixes.
Full-point BEIR-15 nDCG@10: 0.453.
Load with the Tevatron-Elastic framework and select an operating point with prune_to /
encode_at; see the repository for usage. Part of a release of 20 checkpoints (3 backbones,
retrieval and reranking, all compression axes) accompanying the Tevatron-Elastic paper. Reported as
a reproducibility resource, not a state-of-the-art claim.