Instructions to use vocab-transformers/cross_encoder-msmarco-distilbert-word2vec256k-MLM_400k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vocab-transformers/cross_encoder-msmarco-distilbert-word2vec256k-MLM_400k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vocab-transformers/cross_encoder-msmarco-distilbert-word2vec256k-MLM_400k")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vocab-transformers/cross_encoder-msmarco-distilbert-word2vec256k-MLM_400k") model = AutoModelForSequenceClassification.from_pretrained("vocab-transformers/cross_encoder-msmarco-distilbert-word2vec256k-MLM_400k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
#cross_encoder-msmarco-distilbert-word2vec256k-MLM_400k
This CrossEncoder was trained with MarginMSE loss from the vocab-transformers/msmarco-distilbert-word2vec256k-MLM_400k checkpoint. Word embedding matrix has been frozen during training.
You can load the model with sentence-transformers:
from sentence_transformers import CrossEncoder
from torch import nn
model = CrossEncoder(model_name, default_activation_function=nn.Identity())
Performance on TREC Deep Learning (nDCG@10):
- TREC-DL 19: 72.62
- TREC-DL 20: 73.22
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