Update README.md

README.md

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----
-license: mit
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+---
+license: mit
+datasets:
+- imirandam/TROHN-Img
+---
+
+# Model Card for CLIP_Detectos
+## 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_Detector is a model presented in the [BiVLC](https://github.com/IMirandaM/BiVLC) paper for experimentation. It has been trained with the OpenCLIP framework using the CLIP ViT-B-32 model pre-trained by 'openai' as a basis. The encoders are kept frozen, and a sigmoid neuron is added on top of each encoder (more details in the paper). The objective of the model is to classify text and images as natural or synthetic. Hyperparameters:
+
+* Learning rate: 1e-6.
+* Optimizer: Adam optimizer with beta1 = 0.9, beta2 = 0.999, eps = 1e-08 and without weight decay.
+* Loss function: Binary cross-entropy loss (BCELoss).
+* Batch size: We define a batch size of 400.
+* Epochs: We trained the text detector over 10 epochs and the image detectors over 1 epoch. We used validation accuracy as the model selection criterion, i.e. we selected the model with highest accuracy in the corresponding validation set.
+* Data: Then sigmoid neuron is trained with [TROHN-Img](https://huggingface.co/datasets/imirandam/TROHN-Img) dataset.
+
+### 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={}
+}
+```