Update README.md

README.md

@@ -58,4 +58,273 @@ Example QA:
 - Training diagnostic QA systems for auscultation sounds  
 - Benchmarking audio-language models in healthcare  
 - Studying generalization across unseen respiratory/cardiac datasets
+
+
+---
+license: <!-- [CUSTOMIZE THIS] Dataset license, e.g. "cc-by-4.0", "cc-by-sa-4.0", "apache-2.0", "mit", etc. -->
+language:
+- <!-- [CUSTOMIZE THIS] Primary language of the dataset, e.g. "en", "zh", "multilingual" -->
+multilinguality:
+- <!-- [CUSTOMIZE THIS] Whether the dataset is monolingual, multilingual, or translation, e.g. "monolingual", "multilingual", "translation" -->
+size_categories:
+- <!-- [CUSTOMIZE THIS] Size category of the dataset, e.g. "10K<n<100K", "100K<n<1M", "1M<n<10M", etc. -->
+source_datasets:
+- <!-- [CUSTOMIZE THIS] Source of the dataset, e.g. "original", "extended", "derived" -->
+task_categories:
+- <!-- [CUSTOMIZE THIS] Task categories, e.g. "text-classification", "question-answering", "image-classification", etc. -->
+task_ids:
+- <!-- [CUSTOMIZE THIS] Specific task IDs, e.g. "sentiment-classification", "topic-classification", etc. -->
+paperswithcode_id: <!-- [CUSTOMIZE THIS] Optional: paperswithcode ID if applicable -->
+dataset_info:
+  features:
+    - name: <!-- [CUSTOMIZE THIS] Feature name, e.g. "text" -->
+      dtype: <!-- [CUSTOMIZE THIS] Data type, e.g. "string" -->
+    - name: <!-- [CUSTOMIZE THIS] Feature name, e.g. "label" -->
+      dtype: <!-- [CUSTOMIZE THIS] Data type, e.g. "int64" -->
+    # Add more features as needed
+  config_name: <!-- [CUSTOMIZE THIS] Configuration name, e.g. "default" -->
+  splits:
+    - name: train
+      num_examples: <!-- [CUSTOMIZE THIS] Number of examples in train split, e.g. 8000 -->
+    - name: validation
+      num_examples: <!-- [CUSTOMIZE THIS] Number of examples in validation split, e.g. 1000 -->
+    - name: test
+      num_examples: <!-- [CUSTOMIZE THIS] Number of examples in test split, e.g. 1000 -->
+pretty_name: <!-- [CUSTOMIZE THIS] A human-readable name for the dataset -->
+---
+
+# <!-- [CUSTOMIZE THIS] Dataset Name -->
+
+## Dataset Description
+
+### Dataset Summary
+
+<!-- [CUSTOMIZE THIS] Provide a short introduction to the dataset, including:
+- What is this dataset about?
+- What tasks does it support?
+- How was it created?
+- What makes it unique or valuable? -->
+
+### Supported Tasks and Leaderboards
+
+<!-- [CUSTOMIZE THIS] Describe the tasks this dataset supports:
+- What tasks can be performed on this dataset? (e.g., classification, Q&A, etc.)
+- Are there leaderboards associated with this dataset?
+- What metrics are appropriate for evaluating models on this dataset? -->
+
+### Languages
+
+<!-- [CUSTOMIZE THIS] Specify the languages used in the dataset and any relevant language-specific information -->
+
+## Dataset Structure
+
+### Data Instances
+
+<!-- [CUSTOMIZE THIS] Provide examples of data instances from the dataset. Include:
+- A description of what each instance represents
+- One or more concrete examples in JSON or dictionary format -->
+
+```python
+# Example data instance
+{
+    'feature1': 'value1',
+    'feature2': 'value2',
+    'label': 0
+}
+```
+
+### Data Fields
+
+<!-- [CUSTOMIZE THIS] Describe all data fields, including:
+- Field name
+- Data type
+- Description of what the field represents
+- For categorical fields, the possible values and their meanings -->
+
+- `feature1`: a `string` feature representing <!-- description -->
+- `feature2`: a `string` feature representing <!-- description -->
+- `label`: a `int64` classification label, with 0 indicating <!-- meaning --> and 1 indicating <!-- meaning -->
+
+### Data Splits
+
+<!-- [CUSTOMIZE THIS] Describe how the data is split:
+- Number of instances in each split (train/validation/test)
+- Criteria used for splitting the data
+- Whether the splits are balanced or representative -->
+
+## Dataset Creation
+
+### Curation Rationale
+
+<!-- [CUSTOMIZE THIS] Explain why this dataset was created:
+- What need does it address?
+- What gaps in existing datasets does it fill?
+- What research questions was it designed to help answer? -->
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+<!-- [CUSTOMIZE THIS] Describe how the initial data was collected:
+- What sources were used?
+- What collection process was followed?
+- How was the data normalized or standardized? -->
+
+#### Who are the source language producers?
+
+<!-- [CUSTOMIZE THIS] Describe who produced the original data:
+- Was it written/created by professionals, crowdworkers, experts in a domain?
+- Is it from a specific demographic or community?
+- What motivated the original authors/speakers? -->
+
+### Annotations
+
+#### Annotation process
+
+<!-- [CUSTOMIZE THIS] If the dataset includes annotations, describe:
+- How annotations were created (expert labeling, crowdsourcing, etc.)
+- Annotation guidelines provided to annotators
+- Quality control measures -->
+
+#### Who are the annotators?
+
+<!-- [CUSTOMIZE THIS] Describe who performed the annotations:
+- Professional annotators, crowdworkers, domain experts?
+- Demographic information if relevant
+- How were annotators compensated? -->
+
+### Personal and Sensitive Information
+
+<!-- [CUSTOMIZE THIS] Describe handling of personal information:
+- Does the dataset contain personal information?
+- What steps were taken to protect privacy?
+- Were individuals notified or did they consent to data collection? -->
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+<!-- [CUSTOMIZE THIS] Consider the social impact:
+- How might this dataset benefit society?
+- Are there potential risks or harms from using this dataset?
+- Are there specific applications that should be encouraged or discouraged? -->
+
+### Discussion of Biases
+
+<!-- [CUSTOMIZE THIS] Discuss potential biases:
+- What biases might be present in the data?
+- How might these biases affect models trained on this data?
+- What steps were taken to mitigate biases? -->
+
+### Other Known Limitations
+
+<!-- [CUSTOMIZE THIS] Describe any other limitations:
+- Coverage limitations
+- Technical limitations
+- Areas where the dataset may not perform well -->
+
+## Additional Information
+
+### Dataset Curators
+
+<!-- [CUSTOMIZE THIS] Information about the dataset curators:
+- Who created this dataset?
+- Institutional affiliations
+- Contact information if appropriate -->
+
+### Licensing Information
+
+<!-- [CUSTOMIZE THIS] Detail the licensing:
+- What license covers this dataset?
+- Any restrictions on use
+- Attribution requirements -->
+
+### Citation Information
+
+<!-- [CUSTOMIZE THIS] Provide citation information:
+- How should this dataset be cited?
+- BibTeX citation -->
+
+```
+@inproceedings{
+  author = {Author1 LastName, Author2 LastName},
+  title = {Dataset Title},
+  booktitle = {Conference or Journal Name},
+  year = {20XX},
+  url = {URL to paper or dataset}
+}
+```
+
+### Contributions
+
+<!-- [CUSTOMIZE THIS] Acknowledge contributions:
+- Who contributed to this dataset card?
+- Thanks to reviewers or other contributors -->
+
+## How to Use
+
+### Loading the Dataset
+
+```python
+# Example code to load the dataset
+from datasets import load_dataset
+
+dataset = load_dataset("username/dataset_name")
+
+# Access splits
+train_data = dataset["train"]
+validation_data = dataset["validation"]
+test_data = dataset["test"]
+
+# Example usage
+for example in train_data.select(range(3)):
+    print(example)
+```
+
+### Example Preprocessing and Training
+
+```python
+# Example preprocessing and model training code
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
+
+# Load tokenizer and tokenize data
+tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
+
+def tokenize_function(examples):
+    return tokenizer(examples["feature1"], padding="max_length", truncation=True)
+
+tokenized_dataset = dataset.map(tokenize_function, batched=True)
+
+# Define model
+model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2)
+
+# Define training arguments
+training_args = TrainingArguments(
+    output_dir="./results",
+    per_device_train_batch_size=16,
+    per_device_eval_batch_size=16,
+    num_train_epochs=3,
+    evaluation_strategy="epoch",
+    save_strategy="epoch",
+    load_best_model_at_end=True,
+)
+
+# Define trainer
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_dataset["train"],
+    eval_dataset=tokenized_dataset["validation"],
+)
+
+# Train model
+trainer.train()
+```
+
+### Community and Support
+
+<!-- [CUSTOMIZE THIS] Information on how to get help with the dataset:
+- Links to community forums
+- Ways to report issues or contribute improvements
+- Contact information for maintainers -->