Sentence Similarity
Transformers
PyTorch
ONNX
Safetensors
sentence-transformers
Transformers.js
English
modernbert
feature-extraction
mteb
embedding
text-embeddings-inference
Instructions to use Alibaba-NLP/gte-modernbert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Alibaba-NLP/gte-modernbert-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Alibaba-NLP/gte-modernbert-base") model = AutoModel.from_pretrained("Alibaba-NLP/gte-modernbert-base", device_map="auto") - sentence-transformers
How to use Alibaba-NLP/gte-modernbert-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Alibaba-NLP/gte-modernbert-base") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers.js
How to use Alibaba-NLP/gte-modernbert-base with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('sentence-similarity', 'Alibaba-NLP/gte-modernbert-base'); - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -130,7 +130,7 @@ The results of other models are retrieved from [MTEB leaderboard](https://huggin
|
|
| 130 |
| [**gte-modernbert-base**](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) | 149 | 1024 | 8192 | **64.38** | **76.99** | **46.47** | **85.93** | **59.24** | **55.33** | **81.57** | **30.68** |
|
| 131 |
|
| 132 |
|
| 133 |
-
### LoCo (Long Document Retrieval)
|
| 134 |
|
| 135 |
| Model Name | Dimension | Sequence Length | Average (5) | QsmsumRetrieval | SummScreenRetrieval | QasperAbastractRetrieval | QasperTitleRetrieval | GovReportRetrieval |
|
| 136 |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|
|
@@ -140,14 +140,14 @@ The results of other models are retrieved from [MTEB leaderboard](https://huggin
|
|
| 140 |
| [gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) | 768 | 8192 | 88.88 | 54.45 | 93.00 | 99.82 | 98.03 | 98.70 |
|
| 141 |
| [gte-reranker-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) | - | 8192 | 90.68 | 70.86 | 94.06 | 99.73 | 99.11 | 89.67 |
|
| 142 |
|
| 143 |
-
### COIR (Code Retrieval Task)
|
| 144 |
|
| 145 |
| Model Name | Dimension | Sequence Length | Average(20) | CodeSearchNet-ccr-go | CodeSearchNet-ccr-java | CodeSearchNet-ccr-javascript | CodeSearchNet-ccr-php | CodeSearchNet-ccr-python | CodeSearchNet-ccr-ruby | CodeSearchNet-go | CodeSearchNet-java | CodeSearchNet-javascript | CodeSearchNet-php | CodeSearchNet-python | CodeSearchNet-ruby | apps | codefeedback-mt | codefeedback-st | codetrans-contest | codetrans-dl | cosqa | stackoverflow-qa | synthetic-text2sql |
|
| 146 |
|:----:|:---:|:---:|:---:|:---:| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
|
| 147 |
| [gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) | 768 | 8192 | 79.31 | 94.15 | 93.57 | 94.27 | 91.51 | 93.93 | 90.63 | 88.32 | 83.27 | 76.05 | 85.12 | 88.16 | 77.59 | 57.54 | 82.34 | 85.95 | 71.89 | 35.46 | 43.47 | 91.2 | 61.87 |
|
| 148 |
| [gte-reranker-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) | - | 8192 | 79.99 | 96.43 | 96.88 | 98.32 | 91.81 | 97.7 | 91.96 | 88.81 | 79.71 | 76.27 | 89.39 | 98.37 | 84.11 | 47.57 | 83.37 | 88.91 | 49.66 | 36.36 | 44.37 | 89.58 | 64.21 |
|
| 149 |
|
| 150 |
-
### BEIR
|
| 151 |
|
| 152 |
| Model Name | Dimension | Sequence Length | Average(15) | ArguAna | ClimateFEVER | CQADupstackAndroidRetrieval | DBPedia | FEVER | FiQA2018 | HotpotQA | MSMARCO | NFCorpus | NQ | QuoraRetrieval | SCIDOCS | SciFact | Touche2020 | TRECCOVID |
|
| 153 |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
|
|
|
|
| 130 |
| [**gte-modernbert-base**](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) | 149 | 1024 | 8192 | **64.38** | **76.99** | **46.47** | **85.93** | **59.24** | **55.33** | **81.57** | **30.68** |
|
| 131 |
|
| 132 |
|
| 133 |
+
### LoCo (Long Document Retrieval)(NDCG@10)
|
| 134 |
|
| 135 |
| Model Name | Dimension | Sequence Length | Average (5) | QsmsumRetrieval | SummScreenRetrieval | QasperAbastractRetrieval | QasperTitleRetrieval | GovReportRetrieval |
|
| 136 |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|
|
|
|
| 140 |
| [gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) | 768 | 8192 | 88.88 | 54.45 | 93.00 | 99.82 | 98.03 | 98.70 |
|
| 141 |
| [gte-reranker-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) | - | 8192 | 90.68 | 70.86 | 94.06 | 99.73 | 99.11 | 89.67 |
|
| 142 |
|
| 143 |
+
### COIR (Code Retrieval Task)(NDCG@10)
|
| 144 |
|
| 145 |
| Model Name | Dimension | Sequence Length | Average(20) | CodeSearchNet-ccr-go | CodeSearchNet-ccr-java | CodeSearchNet-ccr-javascript | CodeSearchNet-ccr-php | CodeSearchNet-ccr-python | CodeSearchNet-ccr-ruby | CodeSearchNet-go | CodeSearchNet-java | CodeSearchNet-javascript | CodeSearchNet-php | CodeSearchNet-python | CodeSearchNet-ruby | apps | codefeedback-mt | codefeedback-st | codetrans-contest | codetrans-dl | cosqa | stackoverflow-qa | synthetic-text2sql |
|
| 146 |
|:----:|:---:|:---:|:---:|:---:| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
|
| 147 |
| [gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) | 768 | 8192 | 79.31 | 94.15 | 93.57 | 94.27 | 91.51 | 93.93 | 90.63 | 88.32 | 83.27 | 76.05 | 85.12 | 88.16 | 77.59 | 57.54 | 82.34 | 85.95 | 71.89 | 35.46 | 43.47 | 91.2 | 61.87 |
|
| 148 |
| [gte-reranker-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) | - | 8192 | 79.99 | 96.43 | 96.88 | 98.32 | 91.81 | 97.7 | 91.96 | 88.81 | 79.71 | 76.27 | 89.39 | 98.37 | 84.11 | 47.57 | 83.37 | 88.91 | 49.66 | 36.36 | 44.37 | 89.58 | 64.21 |
|
| 149 |
|
| 150 |
+
### BEIR(NDCG@10)
|
| 151 |
|
| 152 |
| Model Name | Dimension | Sequence Length | Average(15) | ArguAna | ClimateFEVER | CQADupstackAndroidRetrieval | DBPedia | FEVER | FiQA2018 | HotpotQA | MSMARCO | NFCorpus | NQ | QuoraRetrieval | SCIDOCS | SciFact | Touche2020 | TRECCOVID |
|
| 153 |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
|