distilbert-base-uncased trained on MSMARCO Document Reranking task, #### usage ``` from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained('brutusxu/distilbert-base-cross-encoder-first-p') model = AutoModelForSequenceClassification.from_pretrained('brutusxu/distilbert-base-cross-encoder-first-p') query = 'I love New York' document = 'I like New York' input = '
' + query + tokenizer.sep_token + '' + document
tokenized_input = tokenizer(input, return_tensors='pt')
ranking_score = model(**tokenized_input)
```
#### performance
on MSMARCO Document Reranking w. top-100 documents from BM25
```
MRR@10: 0.373
MRR@100: 0.381
nDCG@10: 0.442
nDCG@10: 0.475
```