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---
library_name: transformers
tags:
- cross-encoder
datasets:
- lightonai/ms-marco-en-bge
language:
- en
base_model:
- cross-encoder/ms-marco-MiniLM-L-6-v2
---
# Model Card for Model ID
This model is finetuned starting from the well-known [ms-marco-MiniLM-L-6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2) using KL distillation techniques as described [here](https://www.answer.ai/posts/2024-08-13-small-but-mighty-colbert.html),
using [bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) as teacher
# Usage
## Usage with Transformers
```python
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")
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
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder("juanluisdb/MiniLM-L-6-rerank-reborn", max_length=512)
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
```
# Evaluation
### 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-ablated | MiniLM-L-6-rerank-refreshed |
|:---------------|-------:|----------------------------:|:---------------------|:-----------------------|-------------------------:|--------------------------------------:|:------------------------------|
| nq | 0.305 | 0.533 | **0.597** | 0.535 | 0.523 | 0.541 | 0.580 |
| fever | 0.638 | 0.852 | 0.857 | 0.767 | 0.801 | 0.822 | **0.867** |
| fiqa | 0.238 | 0.336 | **0.397** | 0.382 | 0.349 | 0.36 | 0.364 |
| trec-covid | 0.589 | 0.774 | 0.784 | **0.830** | 0.741 | 0.733 | 0.738 |
| scidocs | 0.15 | 0.166 | 0.169 | **0.171** | 0.164 | 0.163 | 0.165 |
| scifact | 0.676 | 0.739 | 0.731 | 0.719 | 0.688 | 0.738 | **0.750** |
| nfcorpus | 0.318 | 0.353 | 0.336 | **0.353** | 0.349 | 0.35 | 0.350 |
| hotpotqa | 0.629 | 0.745 | **0.794** | 0.668 | 0.724 | 0.758 | 0.775 |
| dbpedia-entity | 0.319 | 0.421 | **0.445** | 0.416 | 0.445 | 0.438 | 0.444 |
| quora | 0.787 | 0.858 | 0.858 | 0.747 | 0.825 | 0.862 | **0.871** |
| climate-fever | 0.163 | 0.233 | **0.314** | 0.253 | 0.244 | 0.245 | 0.309 |
| | nq* | fever* | fiqa | trec-covid | scidocs | scifact | nfcorpus | hotpotqa | dbpedia-entity | quora | climate-fever |
|:--------------------------|:----------|:----------|:----------|:-------------|:----------|:----------|:-----------|:-----------|:-----------------|:----------|:----------------|
| bm25 | 0.305 | 0.638 | 0.238 | 0.589 | 0.150 | 0.676 | 0.318 | 0.629 | 0.319 | 0.787 | 0.163 |
| jina-reranker-v1-turbo-en | 0.533 | 0.852 | 0.336 | 0.774 | 0.166 | 0.739 | 0.353 | 0.745 | 0.421 | 0.858 | 0.233 |
| bge-reranker-v2-m3 | **0.597** | 0.857 | **0.397** | 0.784 | 0.169 | 0.731 | 0.336 | **0.794** | **0.445** | 0.858 | **0.314** |
| mxbai-rerank-base-v1 | 0.535 | 0.767 | 0.382 | **0.830** | **0.171** | 0.719 | **0.353** | 0.668 | 0.416 | 0.747 | 0.253 |
| ms-marco-MiniLM-L-6-v2 | 0.523 | 0.801 | 0.349 | 0.741 | 0.164 | 0.688 | 0.349 | 0.724 | 0.445 | 0.825 | 0.244 |
| MiniLM-L-6-rerank-reborn | 0.580 | **0.867** | 0.364 | 0.738 | 0.165 | **0.750** | 0.350 | 0.775 | 0.444 | **0.871** | 0.309 |
\* 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:
| | nq | fever | fiqa | trec-covid | scidocs | scifact | nfcorpus | hotpotqa | dbpedia-entity | quora | climate-fever |
|:------------------------------------|-------:|--------:|-------:|-------------:|----------:|----------:|-----------:|-----------:|-----------------:|--------:|----------------:|
| ms-marco-MiniLM-L-6-v2 | 0.5234 | 0.8007 | 0.349 | 0.741 | 0.1638 | 0.688 | 0.3493 | 0.7235 | 0.4445 | 0.8251 | 0.2438 |
| MiniLM-L-6-rerank-refreshed-ablated | 0.5412 | 0.8221 | 0.3598 | 0.7331 | 0.163 | 0.7376 | 0.3495 | 0.7583 | 0.4382 | 0.8619 | 0.2449 |
| improvement (%) | **3.40** | **2.67** | **3.08** | -1.07 | -0.47 | **7.22** | 0.08 | **4.80** | -1.41 | **4.45** | **0.47** |
# Datasets Used
~900k queries with 32-way triplets were used from these datasets:
* MSMarco
* TriviaQA
* Natural Questions
* FEVER |