Update README.md

README.md

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-license: mit
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+---
+license: mit
+---
+
+# Model Card for CLIP_COCO
+## Model Description
+- **Homepage:** https://imirandam.github.io/BiVLC_project_page/
+- **Repository:** https://github.com/IMirandaM/BiVLC
+- **Paper:**
+- **Point of Contact:** [Imanol Miranda](mailto:imanol.miranda@ehu.eus)
+### Model Summary
+CLIP_COCO is a model presented in the [BiVLC](https://github.com/IMirandaM/BiVLC) paper for experimentation. It has been fine-tuned with OpenCLIP framework using as basis the CLIP ViT-B-32 model pre-trained by 'openai'. The idea behind this fine-tuning is to have a baseline to compare the [CLIP_TROHN-Text](https://huggingface.co/imirandam/CLIP_TROHN-Text) and [CLIP_TROHN-Img](https://huggingface.co/imirandam/CLIP_TROHN-Img) models. Hyperparameters:
+
+* Learning rate: 1e-6.
+* Scheduler: Cosine scheduler with 50 warmup steps.
+* Optimizer: AdamW optimizer with beta1 = 0.9, beta2 = 0.98, eps = 1e-6 and weight decay = 0.1.
+* Loss function: InfoNCE Loss.
+* Batch size: We define a batch size of 400, resulting in 400 images x 400 captions.
+* Epochs: We fine-tune all models over 10 epochs and we used validation accuracy as the model selection criterion, i.e. we selected the model with the highest accuracy on the corresponding validation set.
+* Data: It is fine-tuned with COCO 2017 train split.
+
+### Evaluation Data
+The model is evaluated in [BiVLC](https://huggingface.co/datasets/imirandam/BiVLC).
+
+### Licensing Information
+This work is licensed under a MIT License.
+
+## Citation Information
+If you find this dataset useful, please consider citing our paper:
+```
+@inproceedings{,
+        title={},
+        author={},
+        booktitle={},
+        year={}
+}
+```