Update README.md

README.md

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 # Model Card for PubMedCLIP
 
-PubMedCLIP is a fine-tuned version of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip) for the medical domain trained on a large number of medical 
-image–text pairs obtained from [PubMed](https://pubmed.ncbi.nlm.nih.gov/) articles.
+PubMedCLIP is a fine-tuned version of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip) for the medical domain.
 
-## Model Details
-
-### Model Description
+## Model Description
 PubMedCLIP was trained on the [Radiology Objects in COntext (ROCO)](https://github.com/razorx89/roco-dataset) dataset, a large-scale multimodal medical imaging dataset.
 The ROCO dataset includes diverse imaging modalities (such as ultrasound, X-Ray, MRI, etc.) from various human body regions (such as head, neck, spine, etc.) 
-captured from open-access PubMed articles. The texts used for training PubMedCLIP were taken from the short captions associated with the images in the dataset.
-
-The authors of PubMedCLIP have released three different pre-trained models at this [link](https://1drv.ms/u/s!ApXgPqe9kykTgwD4Np3-f7ODAot8?e=zLVlJ2) using 
-ResNet-50, ResNet-50x4 and ViT32 as image encoders. This repository includes only the ViT32 variant.
-
-### Model Sources
-
-- **Repository:** [Official GitHub Repository](https://github.com/sarahESL/PubMedCLIP)
-- **Paper [optional]:** {{ paper | default("[More Information Needed]", true)}}
-- **Demo [optional]:** {{ demo | default("[More Information Needed]", true)}}
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-{{ direct_use | default("[More Information Needed]", true)}}
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-{{ downstream_use | default("[More Information Needed]", true)}}
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-{{ out_of_scope_use | default("[More Information Needed]", true)}}
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-{{ bias_risks_limitations | default("[More Information Needed]", true)}}
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-{{ bias_recommendations | default("Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", true)}}
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-{{ get_started_code | default("[More Information Needed]", true)}}
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Data 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. -->
-
-{{ training_data | default("[More Information Needed]", true)}}
-
-### Training Procedure 
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-{{ preprocessing | default("[More Information Needed]", true)}}
-
-
-#### Training Hyperparameters
-
-- **Training regime:** {{ training_regime | default("[More Information Needed]", true)}} <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-{{ speeds_sizes_times | default("[More Information Needed]", true)}}
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-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Data Card if possible. -->
-
-{{ testing_data | default("[More Information Needed]", true)}}
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-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-{{ testing_factors | default("[More Information Needed]", true)}}
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-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-{{ testing_metrics | default("[More Information Needed]", true)}}
-
-### Results
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-{{ results | default("[More Information Needed]", true)}}
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-#### Summary
-
-{{ results_summary | default("", true) }}
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-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-{{ model_examination | default("[More Information Needed]", true)}}
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** {{ hardware | default("[More Information Needed]", true)}}
-- **Hours used:** {{ hours_used | default("[More Information Needed]", true)}}
-- **Cloud Provider:** {{ cloud_provider | default("[More Information Needed]", true)}}
-- **Compute Region:** {{ cloud_region | default("[More Information Needed]", true)}}
-- **Carbon Emitted:** {{ co2_emitted | default("[More Information Needed]", true)}}
-
-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-{{ model_specs | default("[More Information Needed]", true)}}
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-### Compute Infrastructure
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-{{ compute_infrastructure | default("[More Information Needed]", true)}}
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-#### Hardware
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-{{ hardware | default("[More Information Needed]", true)}}
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-#### Software
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-{{ software | default("[More Information Needed]", true)}}
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-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-{{ citation_bibtex | default("[More Information Needed]", true)}}
-
-**APA:**
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-{{ citation_apa | default("[More Information Needed]", true)}}
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-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-{{ glossary | default("[More Information Needed]", true)}}
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-## More Information [optional]
-
-{{ more_information | default("[More Information Needed]", true)}}
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-## Model Card Authors [optional]
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-{{ model_card_authors | default("[More Information Needed]", true)}}
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-## Model Card Contact
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-{{ model_card_contact | default("[More Information Needed]", true)}}
-
-
+captured from open-access [PubMed](https://pubmed.ncbi.nlm.nih.gov/) articles. The authors of PubMedCLIP have released three different pre-trained models at 
+this [link](https://1drv.ms/u/s!ApXgPqe9kykTgwD4Np3-f7ODAot8?e=zLVlJ2) which use ResNet-50, ResNet-50x4 and ViT32 as image encoders. 
+This repository includes only the ViT32 variant of the PubMedCLIP model.
+
+- **Repository:** [PubMedCLIP Official GitHub Repository](https://github.com/sarahESL/PubMedCLIP)
+- **Paper:** [Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?](https://arxiv.org/abs/2112.13906)
+- **Dataset:** [Radiology Objects in COntext (ROCO)](https://github.com/razorx89/roco-dataset)
+  
+## Use
+
+```python
+import requests
+from PIL import Image
+
+from transformers import CLIPProcessor, CLIPModel
+
+model = CLIPModel.from_pretrained("flaviagiammarino/pubmed-clip-vit-base-patch32")
+processor = CLIPProcessor.from_pretrained("flaviagiammarino/pubmed-clip-vit-base-patch32")
+
+url = "https://d168r5mdg5gtkq.cloudfront.net/medpix/img/full/synpic9078.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+text = ["Chest X-Ray", "Brain MRI", "Abdominal CT Scan"]
+
+inputs = processor(text=text, images=image, return_tensors="pt", padding=True)
+probs = model(**inputs).logits_per_image.softmax(dim=1)
+```
+
+## Additional Information
+
+### Licensing Information
+The authors have released the model code and pre-trained checkpoints under the [MIT License](https://github.com/sarahESL/PubMedCLIP/blob/main/LICENSE).
+
+### Citation Information
+```
+@article{eslami2021does,
+  title={Does clip benefit visual question answering in the medical domain as much as it does in the general domain?},
+  author={Eslami, Sedigheh and de Melo, Gerard and Meinel, Christoph},
+  journal={arXiv preprint arXiv:2112.13906},
+  year={2021}
+}
+```