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README.md CHANGED
@@ -1,199 +1,172 @@
1
  ---
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- library_name: transformers
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- tags: []
 
 
 
 
 
 
 
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  ---
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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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-
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- ### Model Description
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-
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- <!-- Provide a longer summary of what this model is. -->
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-
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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.
19
-
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
22
- - **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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-
28
- ### Model Sources [optional]
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-
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- <!-- Provide the basic links for the model. -->
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-
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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-
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- ## Uses
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-
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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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-
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- ### Direct Use
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-
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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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-
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- [More Information Needed]
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-
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- ### Downstream Use [optional]
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-
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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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-
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- [More Information Needed]
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-
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- ### Out-of-Scope Use
53
 
54
- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
 
 
 
 
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- [More Information Needed]
57
 
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- ## Bias, Risks, and Limitations
59
 
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
 
61
 
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- [More Information Needed]
63
 
64
- ### Recommendations
 
65
 
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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.
 
 
 
 
69
 
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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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-
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- ### Training Data
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-
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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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-
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- [More Information Needed]
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-
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- ### Training Procedure
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-
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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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-
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- #### Preprocessing [optional]
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-
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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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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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1
  ---
2
+ license: cc-by-nc-sa-4.0
3
+ language:
4
+ - ja
5
+ tags:
6
+ - clip
7
+ - ja
8
+ - japanese
9
+ - japanese-clip
10
+ pipeline_tag: feature-extraction
11
  ---
12
 
13
+ # Japanese CLIP ViT-H/14 (Base)
14
 
15
+ ## Table of Contents
16
 
17
+ 1. [Overview](#overview)
18
+ 1. [Usage](#usage)
19
+ 1. [Model Details](#model-details)
20
+ 1. [Evaluation](#evaluation)
21
+ 1. [Limitations and Biases](#limitations-and-biases)
22
+ 1. [Citation](#citation)
23
+ 1. [See Also](#see-also)
24
+ 1. [Contact Information](#contact-information)
25
 
26
+ ## Overview
27
 
28
+ * **Developed by**: [HAKUHODO Technologies Inc.](https://www.hakuhodo-technologies.co.jp/)
29
+ * **Model type**: Contrastive Language-Image Pre-trained Model
30
+ * **Language(s)**: Japanese
31
+ * **LICENSE**: [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
32
 
33
+ Presented here is a Japanese [CLIP (Contrastive Language-Image Pre-training)](https://arxiv.org/abs/2103.00020) model,
34
+ mapping Japanese texts and images to a unified embedding space.
35
+ Capable of multimodal tasks including zero-shot image classification,
36
+ text-to-image retrieval, and image-to-text retrieval,
37
+ this model extends its utility when integrated with other components,
38
+ contributing to generative models like image-to-text and text-to-image generation.
39
 
40
+ ## Usage
41
 
42
+ ### Dependencies
43
 
44
+ ```bash
45
+ python3 -m pip install pillow sentencepiece torch torchvision transformers
46
+ ```
47
 
48
+ ### Inference
49
 
50
+ The usage is similar to [`CLIPModel`](https://huggingface.co/docs/transformers/model_doc/clip)
51
+ and [`VisionTextDualEncoderModel`](https://huggingface.co/docs/transformers/model_doc/vision-text-dual-encoder).
52
 
53
+ ```python
54
+ import requests
55
+ import torch
56
+ from PIL import Image
57
+ from transformers import AutoModel, AutoProcessor, BatchEncoding
58
 
59
+ # Download
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+ model_name = "hakuhodo-tech/japanese-clip-vit-h-14-bert-base"
61
+ device = "cuda" if torch.cuda.is_available() else "cpu"
62
+ model = AutoModel.from_pretrained(model_name, trust_remote_code=True).to(device)
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+ processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
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65
+ # Prepare raw inputs
66
+ url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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+ image = Image.open(requests.get(url, stream=True).raw)
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69
+ # Process inputs
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+ inputs = processor(
71
+ text=["犬", "猫", "象"],
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+ images=image,
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+ return_tensors="pt",
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+ padding=True,
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+ )
76
 
77
+ # Infer and output
78
+ outputs = model(**BatchEncoding(inputs).to(device))
79
+ probs = outputs.logits_per_image.softmax(dim=1)
80
+ print([f"{x:.2f}" for x in probs.flatten().tolist()]) # ['0.00', '1.00', '0.00']
81
+ ```
82
 
83
+ ## Model Details
 
 
 
 
 
 
 
 
 
 
 
 
 
 
84
 
85
+ ### Components
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+ The model consists of a frozen ViT-H image encoder from
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+ [laion/CLIP-ViT-H-14-laion2B-s32B-b79K](https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K)
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+ and a 12-layer 12-head BERT text encoder initialized from
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+ [rinna/japanese-clip-vit-b-16](https://huggingface.co/rinna/japanese-clip-vit-b-16).
91
 
92
+ ### Training
93
 
94
+ Model training is done by Zhi Wang with 8 A100 (80 GB) GPUs.
95
+ [Locked-image Tuning (LiT)](https://arxiv.org/abs/2111.07991) is adopted.
96
+ See more details in [the paper](https://www.anlp.jp/proceedings/annual_meeting/2024/pdf_dir/B6-5.pdf).
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98
+ ### Dataset
99
 
100
+ The Japanese subset of the [laion2B-multi](https://huggingface.co/datasets/laion/laion2B-multi) dataset containing ~120M image-text pairs.
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102
  ## Evaluation
103
 
104
+ ### Testing Data
105
 
106
+ The 5K evaluation set (val2017) of [MS-COCO](https://cocodataset.org/)
107
+ with [STAIR Captions](http://captions.stair.center/).
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109
+ ### Metrics
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+ Zero-shot image-to-text and text-to-image recall@1, 5, 10.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Results
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+ | | | | | | | |
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+ | :---------------------------------------------------------------------------------------------------------------------- | :------: | :------: | :------: | :------: | :------: | :------: |
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+ | <td colspan=3 align=center>Text Retrieval</td> <td colspan=3 align=center>Image Retrieval</td> |
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+ | | R@1 | R@5 | R@10 | R@1 | R@5 | R@10 |
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+ | [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 |
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+ | [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 |
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+ | [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 |
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+ | [**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 |
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+ | [**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 |
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+ | [**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** |
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+
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+ \* [Japanese Stable CLIP ViT-L/16](https://huggingface.co/stabilityai/japanese-stable-clip-vit-l-16) is excluded for zero-shot retrieval evaluation as
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+ [the model was partially pre-trained with MS-COCO](https://huggingface.co/stabilityai/japanese-stable-clip-vit-l-16#training-dataset).
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+
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+ ## Limitations and Biases
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+
131
+ Despite our data filtering, it is crucial
132
+ to acknowledge the possibility of the training dataset
133
+ containing offensive or inappropriate content.
134
+ Users should be mindful of the potential societal impact
135
+ and ethical considerations associated with the outputs
136
+ generated by the model when deploying in production systems.
137
+ It is recommended not to employ the model for applications
138
+ that have the potential to cause harm or distress
139
+ to individuals or groups.
140
+
141
+ ## Citation
142
+
143
+ If you found this model useful, please consider citing:
144
+
145
+ ```bibtex
146
+ @article{japanese-clip-vit-h,
147
+ author = {王 直 and 細野 健人 and 石塚 湖太 and 奥田 悠太 and 川上 孝介},
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+ journal = {言語処理学会年次大会発表論文集},
149
+ month = {Mar},
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+ pages = {1547--1552},
151
+ 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},
152
+ volume = {30},
153
+ year = {2024}
154
+ }
155
+ ```
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+
157
+ ## See Also
158
+
159
+ * [Japanese CLIP ViT-H/14 (Deeper)](https://huggingface.co/hakuhodo-tech/japanese-clip-vit-h-14-bert-deeper)
160
+ * [Japanese CLIP ViT-H/14 (Wider)](https://huggingface.co/hakuhodo-tech/japanese-clip-vit-h-14-bert-wider)
161
+
162
+ ## Contact Information
163
+
164
+ Please contact
165
+ [hr-koho\@hakuhodo-technologies.co.jp](mailto:[email protected]?subject=Japanese%20CLIP%20ViT-H/14%20Models)
166
+ for questions and comments about the model,
167
+ and/or
168
+ for business and partnership inquiries.
169
+
170
+ お問い合わせは
171
+ [hr-koho\@hakuhodo-technologies.co.jp](mailto:[email protected]?subject=日本語CLIP%20ViT-H/14モデルについて)
172
+ にご連絡ください。