Fine-Tuned Model
fjmgAI/rerank1-210M-EuroBERT
Base Model
EuroBERT/EuroBERT-210m
Fine-Tuning Method
This is a Cross Encoder model finetuned from EuroBERT/EuroBERT-210m using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
Dataset
Description
This dataset is a collection of question-answer pairs, collected from Google.
Fine-Tuning Details
- The model was trained using 578,402 training samples from sentence-transformer.
Cross Encoder Reranking
- Dataset:
gooaq-dev - Evaluated with
CrossEncoderRerankingEvaluatorwith these parameters:{ "at_k": 10, "always_rerank_positives": false }
| Metric | Value |
|---|---|
| map | 0.7097 (+0.1786) |
| mrr@10 | 0.7089 (+0.1850) |
| ndcg@10 | 0.7579 (+0.1667) |
Cross Encoder Reranking
- Datasets:
NanoMSMARCO_R100,NanoNFCorpus_R100andNanoNQ_R100 - Evaluated with
CrossEncoderRerankingEvaluatorwith these parameters:{ "at_k": 10, "always_rerank_positives": true }
| Metric | NanoMSMARCO_R100 | NanoNFCorpus_R100 | NanoNQ_R100 |
|---|---|---|---|
| map | 0.4630 (-0.0266) | 0.3363 (+0.0753) | 0.4738 (+0.0542) |
| mrr@10 | 0.4452 (-0.0323) | 0.5204 (+0.0206) | 0.4783 (+0.0516) |
| ndcg@10 | 0.5106 (-0.0298) | 0.3632 (+0.0381) | 0.5182 (+0.0176) |
Cross Encoder Nano BEIR
- Dataset:
NanoBEIR_R100_mean - Evaluated with
CrossEncoderNanoBEIREvaluatorwith these parameters:{ "dataset_names": [ "msmarco", "nfcorpus", "nq" ], "rerank_k": 100, "at_k": 10, "always_rerank_positives": true }
| Metric | Value |
|---|---|
| map | 0.4244 (+0.0343) |
| mrr@10 | 0.4813 (+0.0133) |
| ndcg@10 | 0.4640 (+0.0086) |
Usage
Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
pip install -U sentence-transformers
Then you can load this model and run inference.
from sentence_transformers import CrossEncoder
# Download from the 🤗 Hub
model = CrossEncoder("fjmgAI/rerank1-210M-EuroBERT", trust_remote_code=True)
# Get scores for pairs of texts
pairs = [
['what are the risks with taking statins?', "['Muscle pain and damage. One of the most common complaints of people taking statins is muscle pain. ... ', 'Liver damage. Occasionally, statin use could cause an increase in the level of enzymes that signal liver inflammation. ... ', 'Increased blood sugar or type 2 diabetes. ... ', 'Neurological side effects.']"],
['what are the risks with taking statins?', 'Doctors discovered that statins can help lower blood pressure, as well as lower cholesterol. Statins are often prescribed to people with high cholesterol. Too much cholesterol in your blood increases your risk of heart attacks and strokes.'],
['what are the risks with taking statins?', 'Lipitor and Crestor are both effective statins that lower levels of “bad” cholesterol and increase levels of “good” cholesterol. While Crestor is the more potent statin, both medications are effective and have slightly different side effects and drug interactions.'],
['what are the risks with taking statins?', "About simvastatin Simvastatin belongs to a group of medicines called statins. It's used to lower cholesterol if you've been diagnosed with high blood cholesterol. It's also taken to prevent heart disease, including heart attacks and strokes."],
['what are the risks with taking statins?', 'Zetia works to lower cholesterol in a new way different from the statins: it inhibits the absorption of cholesterol in the small intestine, whereas the statins work by blocking cholesterol production in the liver.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)
# Or rank different texts based on similarity to a single text
ranks = model.rank(
'what are the risks with taking statins?',
[
"['Muscle pain and damage. One of the most common complaints of people taking statins is muscle pain. ... ', 'Liver damage. Occasionally, statin use could cause an increase in the level of enzymes that signal liver inflammation. ... ', 'Increased blood sugar or type 2 diabetes. ... ', 'Neurological side effects.']",
'Doctors discovered that statins can help lower blood pressure, as well as lower cholesterol. Statins are often prescribed to people with high cholesterol. Too much cholesterol in your blood increases your risk of heart attacks and strokes.',
'Lipitor and Crestor are both effective statins that lower levels of “bad” cholesterol and increase levels of “good” cholesterol. While Crestor is the more potent statin, both medications are effective and have slightly different side effects and drug interactions.',
"About simvastatin Simvastatin belongs to a group of medicines called statins. It's used to lower cholesterol if you've been diagnosed with high blood cholesterol. It's also taken to prevent heart disease, including heart attacks and strokes.",
'Zetia works to lower cholesterol in a new way different from the statins: it inhibits the absorption of cholesterol in the small intestine, whereas the statins work by blocking cholesterol production in the liver.',
]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
Framework Versions
- Python: 3.11.12
- Sentence Transformers: 4.0.2
- Transformers: 4.51.2
- PyTorch: 2.6.0+cu126
- Accelerate: 1.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
Purpose
This tuned reranker model is optimized for Spanish and English applications, prioritizing accurate reordering of results by leveraging semantic similarity through refined embedding comparisons, ideal for enhancing question-answering and document retrieval tasks.
- Developed by: fjmgAI
- License: apache-2.0
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Model tree for martinerrazquin/reranker-ft-excluded-dnsa
Base model
EuroBERT/EuroBERT-210mDataset used to train martinerrazquin/reranker-ft-excluded-dnsa
Evaluation results
- Map on gooaq devself-reported0.710
- Mrr@10 on gooaq devself-reported0.709
- Ndcg@10 on gooaq devself-reported0.758
- Map on NanoMSMARCO R100self-reported0.463
- Mrr@10 on NanoMSMARCO R100self-reported0.445
- Ndcg@10 on NanoMSMARCO R100self-reported0.511
- Map on NanoNFCorpus R100self-reported0.336
- Mrr@10 on NanoNFCorpus R100self-reported0.520
- Ndcg@10 on NanoNFCorpus R100self-reported0.363
- Map on NanoNQ R100self-reported0.474
