Update README.md

README.md

@@ -23,7 +23,7 @@ using [bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) as te
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 model = AutoModelForSequenceClassification.from_pretrained("juanluisdb/MiniLM-L-6-rerank-reborn")
-tokenizer = AutoTokenizer.from_pretrained("juanluisdb/MiniLM-L-6-rerank-reborn")
+tokenizer = AutoTokenizer.from_pretrained("juanluisdb/MiniLM-L-6-rerank-m3")
 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():
@@ -36,7 +36,7 @@ with torch.no_grad():
 
 ```python
 from sentence_transformers import CrossEncoder
-model = CrossEncoder("juanluisdb/MiniLM-L-6-rerank-reborn", max_length=512)
+model = CrossEncoder("juanluisdb/MiniLM-L-6-rerank-m3", max_length=512)
 scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
 ```
 
@@ -45,7 +45,7 @@ scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Que
 ### BEIR (NDCG@10)
 I've run tests on different BEIR datasets. Cross Encoders rerank top100 BM25 results.
 
-|                |   bm25 |   jina-reranker-v1-turbo-en | bge-reranker-v2-m3   | mxbai-rerank-base-v1   |   ms-marco-MiniLM-L-6-v2 | MiniLM-L-6-rerank-refreshed   |
+|                |   bm25 |   jina-reranker-v1-turbo-en | bge-reranker-v2-m3   | mxbai-rerank-base-v1   |   ms-marco-MiniLM-L-6-v2 | MiniLM-L-6-rerank-m3   |
 |:---------------|-------:|----------------------------:|:---------------------|:-----------------------|-------------------------:|:------------------------------|
 | nq*             |  0.305 |                       0.533 | **0.597**            | 0.535                  |                    0.523 | 0.580                         |
 | fever*         |  0.638 |                       0.852 | 0.857                | 0.767                  |                    0.801 | **0.867**                     |
@@ -61,9 +61,9 @@ I've run tests on different BEIR datasets. Cross Encoders rerank top100 BM25 res
 
 \* Training splits of NQ and Fever were used as part of the training data.
 
-Comparison with [ablated model](https://huggingface.co/juanluisdb/MiniLM-L-6-rerank-reborn-ablated/settings) trained only on MSMarco:
+Comparison with [ablated model](https://huggingface.co/juanluisdb/MiniLM-L-6-rerank-m3-ablated) trained only on MSMarco:
 
-|                |   ms-marco-MiniLM-L-6-v2 |   MiniLM-L-6-rerank-refreshed-ablated |
+|                |   ms-marco-MiniLM-L-6-v2 |   MiniLM-L-6-rerank-m3-ablated |
 |:---------------|-------------------------:|--------------------------------------:|
 | nq             |                   0.5234 |                                **0.5412** |
 | fever          |                   0.8007 |                                **0.8221** |