modified readme

README.md

@@ -1,64 +1,7 @@
 ---
 license: apache-2.0
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-# Cross-Encoder for MS Marco
+# fork of Cross-Encoder for MS Marco
+https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-12-v2
 
-This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
-
-The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) for more details. The training code is available here: [SBERT.net Training MS Marco](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/ms_marco)
-
-
-## Usage with Transformers
-
-```python
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch
-
-model = AutoModelForSequenceClassification.from_pretrained('model_name')
-tokenizer = AutoTokenizer.from_pretrained('model_name')
-
-features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'],  padding=True, truncation=True, return_tensors="pt")
-
-model.eval()
-with torch.no_grad():
-    scores = model(**features).logits
-    print(scores)
-```
-
-
-## Usage with SentenceTransformers
-
-The usage becomes easier when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this:
-```python
-from sentence_transformers import CrossEncoder
-model = CrossEncoder('model_name', max_length=512)
-scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
-```
-
-
-## Performance
-In the following table, we provide various pre-trained Cross-Encoders together with their performance on the [TREC Deep Learning 2019](https://microsoft.github.io/TREC-2019-Deep-Learning/) and the [MS Marco Passage Reranking](https://github.com/microsoft/MSMARCO-Passage-Ranking/) dataset. 
-
-
-| Model-Name        | NDCG@10 (TREC DL 19) | MRR@10 (MS Marco Dev)  | Docs / Sec |
-| ------------- |:-------------| -----| --- | 
-| **Version 2 models** | | | 
-| cross-encoder/ms-marco-TinyBERT-L-2-v2 | 69.84 | 32.56 | 9000
-| cross-encoder/ms-marco-MiniLM-L-2-v2 | 71.01 | 34.85 | 4100
-| cross-encoder/ms-marco-MiniLM-L-4-v2 | 73.04 | 37.70 | 2500
-| cross-encoder/ms-marco-MiniLM-L-6-v2 | 74.30 | 39.01 | 1800
-| cross-encoder/ms-marco-MiniLM-L-12-v2 | 74.31 | 39.02 | 960
-| **Version 1 models** | | | 
-| cross-encoder/ms-marco-TinyBERT-L-2  | 67.43 | 30.15  | 9000
-| cross-encoder/ms-marco-TinyBERT-L-4  | 68.09 | 34.50  | 2900
-| cross-encoder/ms-marco-TinyBERT-L-6 |  69.57 | 36.13  | 680
-| cross-encoder/ms-marco-electra-base | 71.99 | 36.41 | 340
-| **Other models** | | | 
-| nboost/pt-tinybert-msmarco | 63.63 | 28.80 | 2900 
-| nboost/pt-bert-base-uncased-msmarco | 70.94 | 34.75 | 340 
-| nboost/pt-bert-large-msmarco | 73.36 | 36.48 | 100 
-| Capreolus/electra-base-msmarco | 71.23 | 36.89 | 340 
-| amberoad/bert-multilingual-passage-reranking-msmarco | 68.40 | 35.54 | 330 
-| sebastian-hofstaetter/distilbert-cat-margin_mse-T2-msmarco | 72.82 | 37.88 | 720
- 
- Note: Runtime was computed on a V100 GPU.
+Added a custom handler to do reranking.