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README.md

@@ -1,199 +1,173 @@
 ---
-library_name: transformers
-tags: []
+license: cc-by-nc-sa-4.0
+language:
+  - ja
+tags:
+  - clip
+  - ja
+  - japanese
+  - japanese-clip
+pipeline_tag: feature-extraction
 ---
 
-# Model Card for Model ID
+# Japanese CLIP ViT-H/14 (Wider)
 
-<!-- Provide a quick summary of what the model is/does. -->
+## Table of Contents
 
+1. [Overview](#overview)
+1. [Usage](#usage)
+1. [Model Details](#model-details)
+1. [Evaluation](#evaluation)
+1. [Limitations and Biases](#limitations-and-biases)
+1. [Citation](#citation)
+1. [See Also](#see-also)
+1. [Contact Information](#contact-information)
 
+## Overview
 
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
+* **Developed by**: [HAKUHODO Technologies Inc.](https://www.hakuhodo-technologies.co.jp/)
+* **Model type**: Contrastive Language-Image Pre-trained Model
+* **Language(s)**: Japanese
+* **LICENSE**: [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+Presented here is a Japanese [CLIP (Contrastive Language-Image Pre-training)](https://arxiv.org/abs/2103.00020) model,
+mapping Japanese texts and images to a unified embedding space.
+Capable of multimodal tasks including zero-shot image classification,
+text-to-image retrieval, and image-to-text retrieval,
+this model extends its utility when integrated with other components,
+contributing to generative models like image-to-text and text-to-image generation.
 
-[More Information Needed]
+## Usage
 
-## Bias, Risks, and Limitations
+### Dependencies
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+```bash
+python3 -m pip install pillow sentencepiece torch torchvision transformers
+```
 
-[More Information Needed]
+### Inference
 
-### Recommendations
+The usage is similar to [`CLIPModel`](https://huggingface.co/docs/transformers/model_doc/clip)
+and [`VisionTextDualEncoderModel`](https://huggingface.co/docs/transformers/model_doc/vision-text-dual-encoder).
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+```python
+import requests
+import torch
+from PIL import Image
+from transformers import AutoModel, AutoProcessor, BatchEncoding
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+# Download
+model_name = "hakuhodo-tech/japanese-clip-vit-h-14-bert-wider"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = AutoModel.from_pretrained(model_name, trust_remote_code=True).to(device)
+processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
 
-## How to Get Started with the Model
+# Prepare raw inputs
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
 
-Use the code below to get started with the model.
+# Process inputs
+inputs = processor(
+    text=["犬", "猫", "象"],
+    images=image,
+    return_tensors="pt",
+    padding=True,
+)
 
-[More Information Needed]
+# Infer and output
+outputs = model(**BatchEncoding(inputs).to(device))
+probs = outputs.logits_per_image.softmax(dim=1)
+print([f"{x:.2f}" for x in probs.flatten().tolist()])  # ['0.00', '1.00', '0.00']
+```
 
-## Training Details
-
-### Training Data
-
-<!-- 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. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
+## Model Details
 
+### Components
 
-#### Training Hyperparameters
+The model consists of a frozen ViT-H image encoder from
+[laion/CLIP-ViT-H-14-laion2B-s32B-b79K](https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K)
+and a 12-layer 24-head BERT text encoder initialized from
+[hakuhodo-tech/japanese-clip-vit-h-14-bert-base](https://huggingface.co/hakuhodo-tech/japanese-clip-vit-h-14-bert-base)
+with [Model Fusion](https://www.anlp.jp/proceedings/annual_meeting/2024/pdf_dir/B6-5.pdf).
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+### Training
 
-#### Speeds, Sizes, Times [optional]
+Model training is done by Zhi Wang with 8 A100 (80 GB) GPUs.
+[Locked-image Tuning (LiT)](https://arxiv.org/abs/2111.07991) is adopted.
+See more details in [the paper](https://www.anlp.jp/proceedings/annual_meeting/2024/pdf_dir/B6-5.pdf).
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+### Dataset
 
-[More Information Needed]
+The Japanese subset of the [laion2B-multi](https://huggingface.co/datasets/laion/laion2B-multi) dataset containing ~120M image-text pairs.
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+### Testing Data
 
-### Testing Data, Factors & Metrics
+The 5K evaluation set (val2017) of [MS-COCO](https://cocodataset.org/)
+with [STAIR Captions](http://captions.stair.center/).
 
-#### Testing Data
+### Metrics
 
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+Zero-shot image-to-text and text-to-image recall@1, 5, 10.
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
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+|                                                                                                                         |          |          |          |          |          |          |
+| :---------------------------------------------------------------------------------------------------------------------- | :------: | :------: | :------: | :------: | :------: | :------: |
+|                                                                                            <td colspan=3 align=center>Text Retrieval</td> <td colspan=3 align=center>Image Retrieval</td> |
+|                                                                                                                         |   R@1    |   R@5    |   R@10   |   R@1    |   R@5    |   R@10   |
+| [recruit-jp/japanese-clip-vit-b-32-roberta-base](https://huggingface.co/recruit-jp/japanese-clip-vit-b-32-roberta-base) |   23.0   |   46.1   |   57.4   |   16.1   |   35.4   |   46.3   |
+| [rinna/japanese-cloob-vit-b-16](https://huggingface.co/rinna/japanese-cloob-vit-b-16)                                   |   37.1   |   63.7   |   74.2   |   25.1   |   48.0   |   58.8   |
+| [rinna/japanese-clip-vit-b-16](https://huggingface.co/rinna/japanese-clip-vit-b-16)                                     |   36.9   |   64.3   |   74.3   |   24.8   |   48.8   |   60.0   |
+| [**Japanese CLIP ViT-H/14 (Base)**](https://huggingface.co/hakuhodo-tech/japanese-clip-vit-h-14-bert-base)              |   39.2   |   66.3   |   76.6   |   28.9   |   53.3   |   63.9   |
+| [**Japanese CLIP ViT-H/14 (Deeper)**](https://huggingface.co/hakuhodo-tech/japanese-clip-vit-h-14-bert-deeper)          | **48.7** |   74.0   |   82.4   |   36.5   |   61.5   |   71.8   |
+| [**Japanese CLIP ViT-H/14 (Wider)**](https://huggingface.co/hakuhodo-tech/japanese-clip-vit-h-14-bert-wider)            |   47.9   | **74.2** | **83.2** | **37.3** | **62.8** | **72.7** |
+
+\* [Japanese Stable CLIP ViT-L/16](https://huggingface.co/stabilityai/japanese-stable-clip-vit-l-16) is excluded for zero-shot retrieval evaluation as
+[the model was partially pre-trained with MS-COCO](https://huggingface.co/stabilityai/japanese-stable-clip-vit-l-16#training-dataset).
+
+## Limitations and Biases
+
+Despite our data filtering, it is crucial
+to acknowledge the possibility of the training dataset
+containing offensive or inappropriate content.
+Users should be mindful of the potential societal impact
+and ethical considerations associated with the outputs
+generated by the model when deploying in production systems.
+It is recommended not to employ the model for applications
+that have the potential to cause harm or distress
+to individuals or groups.
+
+## Citation
+
+If you found this model useful, please consider citing:
+
+```bibtex
+@article{japanese-clip-vit-h,
+ author = {王 直 and 細野 健人 and 石塚 湖太 and 奥田 悠太 and 川上 孝介},
+ journal = {言語処理学会年次大会発表論文集},
+ month = {Mar},
+ pages = {1547--1552},
+ title = {日本語特化の視覚と言語を組み合わせた事前学習モデルの開発 Developing Vision-Language Pre-Trained Models for {J}apanese},
+ volume = {30},
+ year = {2024}
+}
+```
+
+## See Also
+
+* [Japanese CLIP ViT-H/14 (Base)](https://huggingface.co/hakuhodo-tech/japanese-clip-vit-h-14-bert-base)
+* [Japanese CLIP ViT-H/14 (Deeper)](https://huggingface.co/hakuhodo-tech/japanese-clip-vit-h-14-bert-deeper)
+
+## Contact Information
+
+Please contact
+[hr-koho\@hakuhodo-technologies.co.jp](mailto:hr-koho@hakuhodo-technologies.co.jp?subject=Japanese%20CLIP%20ViT-H/14%20Models)
+for questions and comments about the model,
+and/or
+for business and partnership inquiries.
+
+お問い合わせは
+[hr-koho\@hakuhodo-technologies.co.jp](mailto:hr-koho@hakuhodo-technologies.co.jp?subject=日本語CLIP%20ViT-H/14モデルについて)
+にご連絡ください。