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 = '<P>' + query + tokenizer.sep_token + '<Q>' + 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
```
{}
transformers pytorch distilbert text-classification autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us 

distilbert-base-uncased trained on MSMARCO Document Reranking task, 

#### usage


#### performance
on MSMARCO Document Reranking w. top-100 documents from BM25

#### usage #### performance
on MSMARCO Document Reranking w. top-100 documents from BM25
TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us 
 #### usage #### performance
on MSMARCO Document Reranking w. top-100 documents from BM25
TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us 
#### usage#### performance
on MSMARCO Document Reranking w. top-100 documents from BM25