Update README.md

README.md

@@ -1,6 +1,10 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- medical
+license: bsd-3-clause
+language:
+- en
 ---
 
 # Model Card for Model ID
@@ -17,26 +21,21 @@ tags: []
 
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
+- **Developed by:** Umar Igan
+- **Model type:** VLM
+- **Language(s) (NLP):** English
 - **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Finetuned from model [optional]:** Salesforce/blip-image-captioning-base
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
 - **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 
 ## 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. -->
-
+This is a fine-tuned VLM on chest xray medicald dataset, the result shouldn't be used as an advice!!
 ### Direct Use
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
@@ -69,131 +68,52 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset 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. -->
-
-[More Information Needed]
+Example usage:
 
-### Training Procedure
+```python
+from transformers import BlipForConditionalGeneration, AutoProcessor
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+model = BlipForConditionalGeneration.from_pretrained("umarigan/blip-image-captioning-base-chestxray-finetuned").to(device)
+processor = AutoProcessor.from_pretrained("umarigan/blip-image-captioning-base-chestxray-finetuned")
 
-#### Preprocessing [optional]
+inputs = processor(images=image, return_tensors="pt").to(device)
+pixel_values = inputs.pixel_values
 
-[More Information Needed]
+generated_ids = model.generate(pixel_values=pixel_values, max_length=50)
+generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(generated_caption)
+```
+### Training Data
 
+https://huggingface.co/datasets/Shrey-1329/cxiu_hf_dataset
 
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--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. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
+- lr: 5e-5
+- Epoch: 10
+- Dataset size: 1k
 #### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+A simple blip fine-tuned model on medical imaging
 
 ## 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:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
+- **Hardware Type:** GPU
+- **Hours used:** 1
+- **Cloud Provider:** Google
+- **Compute Region:** Frankfurt
+- **Carbon Emitted:** 
 
 ### Compute Infrastructure
 
-[More Information Needed]
+Google Colab L4 GPU
 
 #### Hardware
 
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
+Google Colab L4 GPU
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
 
 ## Model Card Contact
 
-[More Information Needed]
+Umar Igan