Update README.md

README.md

@@ -6,7 +6,7 @@ datasets:
 - StanfordAIMI/interpret-cxr-test-hidden
 ---
 
-# CXRMate-RRG4
+# CXRMate-RRG4: Entropy-Augmented Self-Critical Sequence Training for Radiology Report Generation
 
 This is an evolution of https://huggingface.co/aehrc/cxrmate developed for the Radiology Report Generation task of BioNLP @ ACL 2024.
 
@@ -22,193 +22,36 @@ We use token type embeddings to differentiate between findings and impression se
 To handle missing sections, we employ special tokens. 
 We also utilise an attention mask with non-causal masking for the image embeddings and a causal mask for the report token embeddings.
 
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-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]
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-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
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-- **Finetuned from model [optional]:** [More Information Needed]
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-### Model Sources [optional]
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-<!-- Provide the basic links for the model. -->
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-
-## 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. -->
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
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-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## 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. -->
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-### Training Procedure
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
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-
-#### Training Hyperparameters
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-- **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. -->
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-[More Information Needed]
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-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
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-<!-- This should link to a Dataset Card if possible. -->
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-### Results
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-#### Summary
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-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
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-
-## 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]
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-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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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. -->
+## How to use:
+```
+tokenizer = transformers.AutoTokenizer.from_pretrained('aehrc/cxrmate-rrg24')
+model = transformers.AutoModel.from_pretrained('aehrc/cxrmate-rrg24', trust_remote_code=True)
+transforms = v2.Compose(
+    [
+        v2.PILToTensor(),
+        v2.Grayscale(num_output_channels=3),
+        v2.Resize(size=model.config.encoder.image_size, antialias=True),
+        v2.CenterCrop(size=[model.config.encoder.image_size]*2),
+        v2.ToDtype(torch.float32, scale=True),
+        v2.Normalize(mean=model.config.encoder.image_mean, std=model.config.encoder.image_std),
+    ]
+)
+image = transforms(image)
+output_ids = model.generate(
+    pixel_values=image.unsqueeze(0).unsqueeze(0),
+    max_length=512,
+    bad_words_ids=[[tokenizer.convert_tokens_to_ids('[NF]')], [tokenizer.convert_tokens_to_ids('[NI]')]],
+    num_beams=4,
+    use_cache=True,
+)
+findings, impression = model.split_and_decode_sections(output_ids, tokenizer)
+```
+
+## Paper:
+
+## Citation:
 
 **BibTeX:**
 
 [More Information Needed]
 
-**APA:**
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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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-
-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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