Update README.md

README.md

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 ---
 license: mit
+language:
+- pt
+tags:
+- CAPIVARA
+- Portuguese CLIP
+- Portuguese
+- OpenCLIP
+datasets:
+- conceptual_captions
+- PraCegoVer
+- MS_COCO
+- Flickr30K
+- ImageNet
+- ELEVATER
 ---
+# Model Card for CAPIVARA
+
+CAPIVARA is a cost-efficient framework designed to enhance the performance of multilingual CLIP models in low-resource languages. 
+This model  holds the state of the art in many zero-shot tasks involving images and Portuguese texts.
+
+## Model Details
+
+### Model Description
+
+CAPIVARA is built upon pre-trained [OpenCLIP ViT-B/32 XLM-Roberta Base](https://huggingface.co/laion/CLIP-ViT-B-32-xlm-roberta-base-laion5B-s13B-b90k) and 
+fine-tuned with [Conceptual Captions](https://aclanthology.org/P18-1238.pdf) and synthetic captions generated by [BLIP2](https://huggingface.co/Salesforce/blip2-opt-2.7b-coco).
+All the captions are translated with Google Translator.
+
+
+## Uses
+
+### Direct Use
+
+Zero-shot image classification, zero-shot image and text retrieval, etc.
+
+### Downstream Use
+
+Image classification and other image task fine-tuning, linear probe image classification, 
+image captioning, image generation guiding and conditioning, etc.
+
+
+## Ethical considerations
+
+For ethical considerations, please, read the Model Cards section in the [paper](https://arxiv.org/abs/2310.13683).
+
+## Training Details
+
+### Training Data
+The model was fine-tuned with [Conceptual Captions](https://aclanthology.org/P18-1238.pdf) and synthetic captions generated by [BLIP2](https://huggingface.co/Salesforce/blip2-opt-2.7b-coco).
+All the captions are translated with Google Translator.
+
+#### Training Hyperparameters
+```
+Optimizer: "Adam"
+eps: 1e-8
+weight_decay: 0.2
+betas: [ 0.9, 0.98 ]
+
+LR_scheduler: "CosineWarmupLR"
+min_learning_rate: 1e-7
+max_learning_rate: 5e-7
+warmup_lr: 500
+
+batch_size: 2816
+max_steps: 5863 # 10 epochs
+```
+
+## Evaluation
+
++ [Zero-shot image classification](https://github.com/hiaac-nlp/CAPIVARA/blob/main/clip_pt/src/evaluate/capivara_classification.ipynb)
++ [Zero-shot cross-modal retrieval](https://github.com/hiaac-nlp/CAPIVARA/blob/main/clip_pt/src/evaluate/capivara_retrieval.ipynb)
+
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+For cross-modal retrieval, we used [PraCegoVer](https://www.mdpi.com/2306-5729/7/2/13), which is composed of images annotated originally 
+with Portuguese texts, and our Portuguese-translated versions of [MS COCO](https://link.springer.com/chapter/10.1007/978-3-319-10602-1_48) 
+and [Flickr30k](https://openaccess.thecvf.com/content_iccv_2015/papers/Plummer_Flickr30k_Entities_Collecting_ICCV_2015_paper.pdf). 
+We also translate the labels from [ImageNet](https://ieeexplore.ieee.org/document/5206848) and the  
+[ELEVATER](https://proceedings.neurips.cc/paper_files/paper/2022/hash/3c4688b6a76f25f2311daa0d75a58f1a-Abstract-Datasets_and_Benchmarks.html) 
+benchmark datasets for image classification.
+
+### Results
+
+#### Zero-shot Cross-Modal Retrieval
+
+We conducted zero-shot cross-modal retrieval experiments on Flickr30k and MS COCO with captions
+translated into Portuguese, and PraCegoVer. We report the average and standard deviation for 3 runs.
+
+<table>
+<thead>
+  <tr>
+    <th>Models</th>
+    <th colspan="2">Flickr30k</th>
+    <th colspan="2"> MS COCO</th>
+    <th colspan="2">PraCegoVer</th>
+  </tr>
+</thead>
+<tbody>
+  <tr>
+    <td></td>
+    <td>text-to-image</td>
+    <td> image-to-text</td>
+    <td>text-to-image</td>
+    <td> image-to-text</td>
+    <td>text-to-image</td>
+    <td> image-to-text</td>
+  </tr>
+  <tr>
+    <td>OpenCLIP ViT-B/32 XLM-Roberta Base (Baseline)</td>
+    <td>76.23</td>
+    <td>87.93</td>
+    <td>52.62</td>
+    <td>66.55</td>
+    <td>65.36</td>
+    <td><b>69.43</b></td>
+  </tr>
+  <tr>
+    <td>CAPIVARA</td>
+    <td><b>79.56 ± 0.01</b></td>
+    <td><b>89.95 ± 0.04</b></td>
+    <td><b>56.27 ± 0.01</b></td>
+    <td><b>71.24 ± 0.01</b></td>
+    <td><b>66.40 ± 0.01</b></td>
+    <td>64.75 ± 0.01</td>
+  </tr>
+</tbody>
+</table>
+
+#### Zero-shot image classification
+
+|            Models             | **Caltech-101** | **CIFAR-10** | **CIFAR-100** | **Country-211** | **DTD**      | **EuroSAT**  | **FER-2013** | **FGVC-Aircraft** | **Food-101** | **GTSRB**    | **Hateful-Memes** | **KITTI-Distance** | **MNIST**    | **Oxford Flowers-102** | **Oxford-IIIT Pets** | **PatchCamelyon** | **Rendered-SST2** | **RESISC-45** | **Stanford-Cars** | **PASCAL VOC-2007** | **Average**  | **ImageNet-1k** |
+|:-----------------------:|:---------------:|:------------:|:-------------:|:---------------:|:------------:|:------------:|:------------:|:-----------------:|:------------:|:------------:|:-----------------:|:------------------:|:------------:|:----------------------:|:--------------------:|:-----------------:|:-----------------:|:-------------:|:-----------------:|:-------------------:|:------------:|:---------------:|
+| OpenCLIP ViT-B/32 XLM-Roberta Base (Baseline) | 84.53 ± 0.00    | 93.99 ± 0.00 | 68.44 ± 0.00  | 17.82 ± 0.00    | 41.17 ± 0.00 | 47.16 ± 0.00 | 48.65 ± 0.00 | 26.30 ± 0.00      | 65.06 ± 0.00 | 43.27 ± 0.00 | 56.50 ± 0.00      | 28.41 ± 0.00       | 54.99 ± 0.00 | 50.88 ± 0.00           | 81.56 ± 0.00         | 50.96 ± 0.00      | 54.20 ± 0.00      | 58.51 ± 0.00  | 84.93 ± 0.00      | 82.09 ± 0.00        | 56.97 ± 0.00 | 45.84 ± 0.00    |
+|      CAPIVARA       | 82.97 ± 0.03    | 93.85 ± 0.00 | 69.37 ± 0.01  | 17.61 ± 0.00    | 42.34 ± 0.04 | 47.77 ± 0.02 | 46.68 ± 0.05 | 25.49 ± 0.01      | 64.58 ± 0.01 | 46.34 ± 0.01 | 56.17 ± 0.00      | 33.94 ± 0.13       | 60.14 ± 0.04 | 49.93 ± 0.02           | 79.37 ± 0.00         | 51.71 ± 0.01      | 54.82 ± 0.03      | 59.71 ± 0.01  | 85.10 ± 0.02      | 82.29 ± 0.00        | **57.51 ± 0.02** | **46.06 ± 0.01**    |
+
+## Environmental Impact
+
+- **GPU:** 1 x Quadro RTX 8000 (48 GB)
+- **Hours used:** 31 hours
+- **Compute Region:** Brazil
+- **Carbon footprint:** 0.5 Kg
+- **Energy**: 6.49 kW
+
+
+## Citation
+
+```bibtex
+@inproceedings{santos2023capivara,
+  title={CAPIVARA: Cost-Efficient Approach for Improving Multilingual CLIP Performance on Low-Resource Languages},
+  author={Santos, Gabriel O. dos and Moreira, Diego A. B. and Ferreira, Alef I. and Silva, Jhessica and Pereira, Luiz and Bueno, Pedro and Sousa, Thiago and Maia, Helena and da Silva, N{\'a}dia and Colombini, Esther and Pedrini, Helio and Avila, Sandra},
+  booktitle = "Workshop on Multi-lingual Representation Learning (MRL), Conference on Empirical Methods in Natural Language Processing (EMNLP)",
+  year = "2023"
+}
+```