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{
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"word_embedding_dimension": 768,
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"pooling_mode_cls_token": true,
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"pooling_mode_mean_tokens": false,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false,
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"include_prompt": true
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}
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210
README.md
210
README.md
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@ -1,3 +1,211 @@
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---
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license: apache-2.0
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language:
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- en
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base_model:
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- answerdotai/ModernBERT-base
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pipeline_tag: sentence-similarity
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library_name: transformers
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tags:
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- sentence-transformers
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- mteb
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- embedding
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- transformers.js
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---
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# gte-modernbert-base
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gte-modernbert-base
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We are excited to introduce the `gte-modernbert` series of models, which are built upon the latest modernBERT pre-trained encoder-only foundation models. The `gte-modernbert` series models include both text embedding models and rerank models.
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The `gte-modernbert` models demonstrates competitive performance in several text embedding and text retrieval evaluation tasks when compared to similar-scale models from the current open-source community. This includes assessments such as MTEB, LoCO, and COIR evaluation.
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## Model Overview
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- Developed by: Tongyi Lab, Alibaba Group
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- Model Type: Text Embedding
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- Primary Language: English
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- Model Size: 149M
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- Max Input Length: 8192 tokens
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- Output Dimension: 768
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### Model list
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| Models | Language | Model Type | Model Size | Max Seq. Length | Dimension | MTEB-en | BEIR | LoCo | CoIR |
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|:--------------------------------------------------------------------------------------:|:--------:|:----------------------:|:----------:|:---------------:|:---------:|:-------:|:----:|:----:|:----:|
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| [`gte-modernbert-base`](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) | English | text embedding | 149M | 8192 | 768 | 64.38 | 55.33 | 87.57 | 79.31 |
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| [`gte-reranker-modernbert-base`](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) | English | text reranker | 149M | 8192 | - | - | 56.19 | 90.68 | 79.99 |
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## Usage
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> [!TIP]
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> For `transformers` and `sentence-transformers`, if your GPU supports it, the efficient Flash Attention 2 will be used automatically if you have `flash_attn` installed. It is not mandatory.
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>
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> ```bash
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> pip install flash_attn
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> ```
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Use with `transformers`
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```python
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# Requires transformers>=4.48.0
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import torch.nn.functional as F
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from transformers import AutoModel, AutoTokenizer
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input_texts = [
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"what is the capital of China?",
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"how to implement quick sort in python?",
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"Beijing",
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"sorting algorithms"
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]
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model_path = "Alibaba-NLP/gte-modernbert-base"
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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model = AutoModel.from_pretrained(model_path)
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# Tokenize the input texts
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batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='pt')
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outputs = model(**batch_dict)
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embeddings = outputs.last_hidden_state[:, 0]
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# (Optionally) normalize embeddings
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embeddings = F.normalize(embeddings, p=2, dim=1)
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scores = (embeddings[:1] @ embeddings[1:].T) * 100
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print(scores.tolist())
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# [[42.89073944091797, 71.30911254882812, 33.664554595947266]]
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```
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Use with `sentence-transformers`:
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```python
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# Requires transformers>=4.48.0
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from sentence_transformers import SentenceTransformer
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from sentence_transformers.util import cos_sim
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input_texts = [
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"what is the capital of China?",
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"how to implement quick sort in python?",
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"Beijing",
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"sorting algorithms"
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]
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model = SentenceTransformer("Alibaba-NLP/gte-modernbert-base")
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embeddings = model.encode(input_texts)
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print(embeddings.shape)
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# (4, 768)
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similarities = cos_sim(embeddings[0], embeddings[1:])
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print(similarities)
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# tensor([[0.4289, 0.7131, 0.3366]])
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```
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Use with `transformers.js`:
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```js
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// npm i @huggingface/transformers
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import { pipeline, matmul } from "@huggingface/transformers";
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// Create a feature extraction pipeline
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const extractor = await pipeline(
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"feature-extraction",
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"Alibaba-NLP/gte-modernbert-base",
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{ dtype: "fp32" }, // Supported options: "fp32", "fp16", "q8", "q4", "q4f16"
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);
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// Embed queries and documents
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const embeddings = await extractor(
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[
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"what is the capital of China?",
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"how to implement quick sort in python?",
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"Beijing",
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"sorting algorithms",
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],
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{ pooling: "cls", normalize: true },
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);
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// Compute similarity scores
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const similarities = (await matmul(embeddings.slice([0, 1]), embeddings.slice([1, null]).transpose(1, 0))).mul(100);
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console.log(similarities.tolist()); // [[42.89077377319336, 71.30916595458984, 33.66455841064453]]
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```
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## Training Details
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The `gte-modernbert` series of models follows the training scheme of the previous [GTE models](https://huggingface.co/collections/Alibaba-NLP/gte-models-6680f0b13f885cb431e6d469), with the only difference being that the pre-training language model base has been replaced from [GTE-MLM](https://huggingface.co/Alibaba-NLP/gte-en-mlm-base) to [ModernBert](https://huggingface.co/answerdotai/ModernBERT-base). For more training details, please refer to our paper: [mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval](https://aclanthology.org/2024.emnlp-industry.103/)
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## Evaluation
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### MTEB
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The results of other models are retrieved from [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard). Given that all models in the `gte-modernbert` series have a size of less than 1B parameters, we focused exclusively on the results of models under 1B from the MTEB leaderboard.
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| Model Name | Param Size (M) | Dimension | Sequence Length | Average (56) | Class. (12) | Clust. (11) | Pair Class. (3) | Reran. (4) | Retr. (15) | STS (10) | Summ. (1) |
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|:------------------------------------------------------------------------------------------------:|:--------------:|:---------:|:---------------:|:------------:|:-----------:|:---:|:---:|:---:|:---:|:-----------:|:--------:|
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| [mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) | 335 | 1024 | 512 | 64.68 | 75.64 | 46.71 | 87.2 | 60.11 | 54.39 | 85 | 32.71 |
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| [multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) | 560 | 1024 | 514 | 64.41 | 77.56 | 47.1 | 86.19 | 58.58 | 52.47 | 84.78 | 30.39 |
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| [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 335 | 1024 | 512 | 64.23 | 75.97 | 46.08 | 87.12 | 60.03 | 54.29 | 83.11 | 31.61 |
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| [gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | 137 | 768 | 8192 | 64.11 | 77.17 | 46.82 | 85.33 | 57.66 | 54.09 | 81.97 | 31.17 |
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| [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 109 | 768 | 512 | 63.55 | 75.53 | 45.77 | 86.55 | 58.86 | 53.25 | 82.4 | 31.07 |
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| [gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 409 | 1024 | 8192 | 65.39 | 77.75 | 47.95 | 84.63 | 58.50 | 57.91 | 81.43 | 30.91 |
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| [modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) | 149 | 768 | 8192 | 62.62 | 74.31 | 44.98 | 83.96 | 56.42 | 52.89 | 81.78 | 31.39 |
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| [nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) | | 768 | 8192 | 62.28 | 73.55 | 43.93 | 84.61 | 55.78 | 53.01| 81.94 | 30.4 |
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| [gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) | 305 | 768 | 8192 | 61.4 | 70.89 | 44.31 | 84.24 | 57.47 |51.08 | 82.11 | 30.58 |
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| [jina-embeddings-v3](https://huggingface.co/jinaai/jina-embeddings-v3) | 572 | 1024 | 8192 | 65.51 | 82.58 |45.21 |84.01 |58.13 |53.88 | 85.81 | 29.71 |
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| [**gte-modernbert-base**](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) | 149 | 768 | 8192 | **64.38** | **76.99** | **46.47** | **85.93** | **59.24** | **55.33** | **81.57** | **30.68** |
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### LoCo (Long Document Retrieval)(NDCG@10)
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| Model Name | Dimension | Sequence Length | Average (5) | QsmsumRetrieval | SummScreenRetrieval | QasperAbastractRetrieval | QasperTitleRetrieval | GovReportRetrieval |
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|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
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| [gte-qwen1.5-7b](https://huggingface.co/Alibaba-NLP/gte-qwen1.5-7b) | 4096 | 32768 | 87.57 | 49.37 | 93.10 | 99.67 | 97.54 | 98.21 |
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| [gte-large-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-v1.5) |1024 | 8192 | 86.71 | 44.55 | 92.61 | 99.82 | 97.81 | 98.74 |
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| [gte-base-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-v1.5) | 768 | 8192 | 87.44 | 49.91 | 91.78 | 99.82 | 97.13 | 98.58 |
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| [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 |
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| [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 |
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### COIR (Code Retrieval Task)(NDCG@10)
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|
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| 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 |
|
||||
|:----:|:---:|:---:|:---:|:---:| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
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| [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 |
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| [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 |
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### BEIR(NDCG@10)
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| Model Name | Dimension | Sequence Length | Average(15) | ArguAna | ClimateFEVER | CQADupstackAndroidRetrieval | DBPedia | FEVER | FiQA2018 | HotpotQA | MSMARCO | NFCorpus | NQ | QuoraRetrieval | SCIDOCS | SciFact | Touche2020 | TRECCOVID |
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| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
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| [gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) | 768 | 8192 | 55.33 | 72.68 | 37.74 | 42.63 | 41.79 | 91.03 | 48.81 | 69.47 | 40.9 | 36.44 | 57.62 | 88.55 | 21.29 | 77.4 | 21.68 | 81.95 |
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| [gte-reranker-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) | - | 8192 | 56.73 | 69.03 | 37.79 | 44.68 | 47.23 | 94.54 | 49.81 | 78.16 | 45.38 | 30.69 | 64.57 | 87.77 | 20.60 | 73.57 | 27.36 | 79.89 |
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## Hiring
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We have open positions for **Research Interns** and **Full-Time Researchers** to join our team at Tongyi Lab.
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We are seeking passionate individuals with expertise in representation learning, LLM-driven information retrieval, Retrieval-Augmented Generation (RAG), and agent-based systems.
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Our team is located in the vibrant cities of **Beijing** and **Hangzhou**.
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If you are driven by curiosity and eager to make a meaningful impact through your work, we would love to hear from you. Please submit your resume along with a brief introduction to <a href="mailto:dingkun.ldk@alibaba-inc.com">dingkun.ldk@alibaba-inc.com</a>.
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## Citation
|
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If you find our paper or models helpful, feel free to give us a cite.
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```
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@inproceedings{zhang2024mgte,
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title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval},
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author={Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Wen and Dai, Ziqi and Tang, Jialong and Lin, Huan and Yang, Baosong and Xie, Pengjun and Huang, Fei and others},
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booktitle={Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track},
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pages={1393--1412},
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year={2024}
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}
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@article{li2023towards,
|
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title={Towards general text embeddings with multi-stage contrastive learning},
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author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan},
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journal={arXiv preprint arXiv:2308.03281},
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year={2023}
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}
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```
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@ -0,0 +1,44 @@
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{
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"architectures": [
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"ModernBertModel"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
|
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"bos_token_id": 50281,
|
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"classifier_activation": "gelu",
|
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"classifier_bias": false,
|
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"classifier_dropout": 0.0,
|
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"classifier_pooling": "mean",
|
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"cls_token_id": 50281,
|
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"decoder_bias": true,
|
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"deterministic_flash_attn": false,
|
||||
"embedding_dropout": 0.0,
|
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"eos_token_id": 50282,
|
||||
"global_attn_every_n_layers": 3,
|
||||
"global_rope_theta": 160000.0,
|
||||
"gradient_checkpointing": false,
|
||||
"hidden_activation": "gelu",
|
||||
"hidden_size": 768,
|
||||
"initializer_cutoff_factor": 2.0,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 1152,
|
||||
"layer_norm_eps": 1e-05,
|
||||
"local_attention": 128,
|
||||
"local_rope_theta": 10000.0,
|
||||
"max_position_embeddings": 8192,
|
||||
"mlp_bias": false,
|
||||
"mlp_dropout": 0.0,
|
||||
"model_type": "modernbert",
|
||||
"norm_bias": false,
|
||||
"norm_eps": 1e-05,
|
||||
"num_attention_heads": 12,
|
||||
"num_hidden_layers": 22,
|
||||
"pad_token_id": 50283,
|
||||
"position_embedding_type": "absolute",
|
||||
"sep_token_id": 50282,
|
||||
"sparse_pred_ignore_index": -100,
|
||||
"sparse_prediction": false,
|
||||
"torch_dtype": "float16",
|
||||
"transformers_version": "4.48.0.dev0",
|
||||
"vocab_size": 50368
|
||||
}
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|
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@ -0,0 +1,10 @@
|
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{
|
||||
"__version__": {
|
||||
"sentence_transformers": "2.7.0",
|
||||
"transformers": "4.48.0",
|
||||
"pytorch": "2.5.0+cu121"
|
||||
},
|
||||
"prompts": {},
|
||||
"default_prompt_name": null,
|
||||
"similarity_fn_name": "cosine"
|
||||
}
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|
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[
|
||||
{
|
||||
"idx": 0,
|
||||
"name": "0",
|
||||
"path": "",
|
||||
"type": "sentence_transformers.models.Transformer"
|
||||
},
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{
|
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"idx": 1,
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"name": "1",
|
||||
"path": "1_Pooling",
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"type": "sentence_transformers.models.Pooling"
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}
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]
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{
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"cls_token": {
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"content": "[CLS]",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"mask_token": {
|
||||
"content": "[MASK]",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "[PAD]",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"sep_token": {
|
||||
"content": "[SEP]",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "[UNK]",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
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"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
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Load Diff
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{
|
||||
"added_tokens_decoder": {
|
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"0": {
|
||||
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||||
},
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||||
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},
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||||
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||||
},
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||||
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||||
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||||
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},
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||||
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||||
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||||
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||||
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||||
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},
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},
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},
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},
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"50350": {
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},
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"50351": {
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},
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},
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||||
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},
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},
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}
|
||||
},
|
||||
"clean_up_tokenization_spaces": true,
|
||||
"cls_token": "[CLS]",
|
||||
"extra_special_tokens": {},
|
||||
"mask_token": "[MASK]",
|
||||
"model_input_names": [
|
||||
"input_ids",
|
||||
"attention_mask"
|
||||
],
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": "[PAD]",
|
||||
"sep_token": "[SEP]",
|
||||
"tokenizer_class": "PreTrainedTokenizerFast",
|
||||
"unk_token": "[UNK]"
|
||||
}
|
||||
Loading…
Reference in New Issue