Update README.md

README.md

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 # CLIPfa: Connecting Farsi Text and Images
 OpenAI released [`the paper Learning Transferable Visual Models From Natural Language Supervision`](https://arxiv.org/abs/2103.00020) in which they present the CLIP (Contrastive Language–Image Pre-training) model. This model is trained to connect text and images, by matching their corresponding vector representations using a contrastive learning objective. CLIP consists of two separate models, a vision encoder and a text encoder. These were trained on 400 Million images and corresponding captions. We have trained a Farsi (Persian) version of OpenAI's CLIP on a dataset of 400,000 (image, text) pairs. We used [`Farahani's RoBERTa-fa`](https://huggingface.co/m3hrdadfi/roberta-zwnj-wnli-mean-tokens) as the text encoder and [‍‍`ViT‍`](https://huggingface.co/openai/clip-vit-base-patch32) as the vision encoder from Original CLIP and finetuned them.
 
-![CLIPfa image](https://github.com/sajjjadayobi/CLIPfa/blob/main/assets/clipfa.png)
 
-It should be noted that only 400K pairs were used for this training, whereas 4 million pairs were used for the Original CLIP. Also, the training took 30 days across 592 GPUs powered by the V100 chip.
+- It should be noted that only 400K pairs were used for this training, whereas 4 million pairs were used for the Original CLIP. Also, the training took 30 days across 592 GPUs powered by the V100 chip.
  
 
 ## How to use?
@@ -35,66 +34,8 @@ demo = CLIPDemo(vision_encoder, text_encoder, tokenizer)
 demo.compute_text_embeddings(['گاو' ,'اسب' ,'ماهی'])
 demo.compute_image_embeddings(test_df.image_path.to_list())
 ```
-### Image Search:
-```python
-demo.image_search(query='غروب خورشید')
-```
-![](https://github.com/sajjjadayobi/CLIPfa/blob/main/assets/image_search.png)
-
-```python
-demo.image_search(query='جنگل در زمستان برفی')
-```
-![](https://github.com/sajjjadayobi/CLIPfa/blob/main/assets/forest%20in%20winter.png)
-
-### Analogy: 
-```python
-demo.anology('sunset.jpg', additional_text='دریا')
-```
-![](https://github.com/sajjjadayobi/CLIPfa/blob/main/assets/analogy-sea.png)
-
-```python
-demo.anology('sunset.jpg', additional_text='برف')
-```
-![](https://github.com/sajjjadayobi/CLIPfa/blob/main/assets/analogy-snow.png)
-
-### Zero Shot Image Classification:
-```python
-demo.zero_shot(image_path='apples.jpg')
-```
-- Provided labels with their probability for each image.
-
-|                   گاو:36 , ماهی:22, اسب:42                   |                   گاو:41 , ماهی:23, اسب:36                   |                 گاو:26 , ماهی:**45**, اسب:27                 |
-| :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
-| ![image](https://github.com/sajjjadayobi/CLIPfa/blob/main/assets/horse.jpg) | ![image](https://github.com/sajjjadayobi/CLIPfa/blob/main/assets/cow.jpg) | ![image](https://github.com/sajjjadayobi/CLIPfa/blob/main/assets/fish.jpg) |
 
 ## Online Demo: [CLIPfa at Huggingface🤗 spaces](https://huggingface.co/spaces/SajjadAyoubi/CLIPfa-Demo)
 We used a small set of images (25K) to keep this app almost real-time, but it's obvious that the quality of image search depends heavily on the size of the image database. 
 
-![](https://github.com/sajjjadayobi/CLIPfa/blob/main/assets/hf-spaces.png)
-
-
-## Dataset: 400K
-We started with this question that how much the original Clip model depends on its big training dataset containing a lot of conceptual samples. Our model shows that It is possible to meet an acceptable enough target with only a little amount of data even though, It may not have known enough concepts and subjects to be used widely. Our model trained on a dataset gathered from different resources such as The Flickr30k, MS-COCO 2017, Google CCm3, ... . We used these datasets and translated them into the Persian language with a [`tool`](https://github.com/sajjjadayobi/CLIPfa/blob/main/clipfa/data/translation.py) prepared by ourselves. Using the Google Translate and Multilingual Similarity Check method we provided an automatic translator that has been given a list of English captions and filtered by the best translations.
-
-- Note: We used [`image2ds`](https://github.com/rom1504/img2dataset) a great tool to download large scale image datasets such as MS-COCO. It can download, resize and package 100M URLs in 20h on one machine. Also supports saving captions for url+caption datasets.
-- [`coco-flickr-fa 130K on Kaggle`](https://www.kaggle.com/navidkanaani/coco-flickr-farsi)
-
-
-## Training: <a href="https://colab.research.google.com/github/sajjjadayobi/CLIPfa/blob/main/notebook/CLIPfa_Training.ipynb"><img src="https://img.shields.io/static/v1?label=%F0%9F%A4%97%20Hugging%20Face&message=CLIPfa Training&color=white"></a>
-Any dataset can be used with little change by the [`training code`](https://github.com/sajjjadayobi/CLIPfa/tree/main/clipfa). CLIPfa can be trained with other encoders as long as they have the same hidden size at the last layer.  In [`this`](https://github.com/sajjjadayobi/CLIPfa/blob/main/notebook/CLIPfa_Training.ipynb) notebook I used [`training code`](https://github.com/sajjjadayobi/CLIPfa/tree/main/clipfa) to train a small CLIP on translated [‍`flickr30K`](https://www.kaggle.com/sajjadayobi360/flickrfa) dataset.
-
-
-## Citation: ↩️
-If you have a technical question regarding the model, code or publication, create an issue in the repository.
-we didn't publish any papers on the work. However, if you did, please cite us properly with an entry like one below.
-```bibtex
-@misc{ParsBigBird,
-  author          = {Sajjad Ayoubi, Navid Kanaani},
-  title           = {CLIPfa: Connecting Farsi Text and Images},
-  year            = 2021,
-  publisher       = {GitHub},
-  journal         = {GitHub repository},
-  howpublished    = {\url{https://github.com/SajjjadAyobi/CLIPfa}},
-}
-```
 > Made with ❤️ in my basement🤫