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Vintern-Embedding-1B

Visual Document Retrieval
Transformers
Safetensors
Vietnamese
English
Chinese
internvl_chat
feature-extraction
custom_code
Model card Files
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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  ## Model Details
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  ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- ## Evaluation
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- ### Testing Data, Factors & Metrics
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- #### Metrics
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ## Citation [optional]
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- **BibTeX:**
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- ## Glossary [optional]
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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  ## Model Details
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  ### Model Description
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+ | Model Name | MAP@5 ↑ | MRR@5 ↑ | NDCG@5 ↑ | Recall@5 ↑ | Mean ↑ |
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+ |----------------------------------------|---------|---------|----------|------------|--------|
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+ | **Multilingual Embedding models** | | | | | |
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+ | me5_small | 33.75 | 33.75 | 35.68 | 41.49 | 36.17 |
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+ | me5_large | 38.16 | 38.16 | 40.27 | 46.62 | 40.80 |
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+ | M3-Embedding | 36.52 | 36.52 | 38.60 | 44.84 | 39.12 |
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+ | OpenAI-embedding-v3 | 30.61 | 30.61 | 32.57 | 38.46 | 33.06 |
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+ | **Vietnamese Embedding models (Prior Work)** | | | | | |
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+ | halong-embedding | 32.15 | 32.15 | 34.13 | 40.09 | 34.63 |
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+ | sup-SimCSE-VietNamese-phobert_base | 10.90 | 10.90 | 12.03 | 15.41 | 12.31 |
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+ | vietnamese-bi-encoder | 13.61 | 13.61 | 14.63 | 17.68 | 14.89 |
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+ | **GreenNode-Embedding** | | | | | |
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+ | M3-GN-VN | 41.85 | 41.85 | 44.15 | 57.05 | 46.23|
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+ | M3-GN-VN-Mixed | 42.08 | 42.08 | 44.33 | 51.06 | 44.89 |
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+ | **Ours** | | | | | |
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+ | Vintern-Embedding-1B | 57.24 | 57.24 | 59.39 | 65.83 | 59.93 |
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+ | Model Name | MAP@5 ↑ | MRR@5 ↑ | NDCG@5 ↑ | Recall@5 ↑ | Mean ↑ |
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+ |----------------------------------------|---------|---------|----------|------------|--------|
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+ | **Multilingual Embedding models** | | | | | |
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+ | me5_small | 54.68 | 54.37 | 58.32 | 69.16 | 59.13 |
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+ | me5_large | 60.14 | 59.62 | 64.17 | 76.02 | 64.99 |
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+ | M3-Embedding | 69.34 | 68.96 | 73.70 | 86.68 | 74.67 |
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+ | OpenAI-embedding-v3 | 38.68 | 38.80 | 41.53 | 49.94 | 41.74 |
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+ | **Vietnamese Embedding models (Prior Work)** | | | | | |
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+ | halong-embedding | 52.57 | 52.28 | 56.64 | 68.72 | 57.55 |
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+ | sup-SimCSE-VietNamese-phobert_base | 25.15 | 25.07 | 27.81 | 35.79 | 28.46 |
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+ | vietnamese-bi-encoder | 54.88 | 54.47 | 59.10 | 79.51 | 61.99 |
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+ | **GreenNode-Embedding** | | | | | |
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+ | M3-GN-VN | 65.03 | 64.80 | 69.19 | 81.66 | 70.17 |
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+ | M3-GN-VN-Mixed | 69.75 | 69.28 | 74.01 | 86.74 | 74.95 |
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+ | **Ours** | | | | | |
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+ | Vintern-Embedding-1B | 68.46 | 68.60 | 72.15 | 82.99 | 73.05 |
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6336b5c831efcb5647f00170/gtr7DMhjV2PHIDhjPy8zH.png)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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