# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("castorini/tct_colbert-v2-hnp-msmarco")
model = AutoModel.from_pretrained("castorini/tct_colbert-v2-hnp-msmarco", device_map="auto")Quick Links
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
This model is to reproduce a variant of TCT-ColBERT-V2 dense retrieval models described in the following paper:
Sheng-Chieh Lin, Jheng-Hong Yang, and Jimmy Lin. In-Batch Negatives for Knowledge Distillation with Tightly-CoupledTeachers for Dense Retrieval. RepL4NLP 2021.
You can find our reproduction report in Pyserini here.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="castorini/tct_colbert-v2-hnp-msmarco")