Update README.md

README.md

@@ -1,19 +1,87 @@
 ---
-license: other
+license: cc-by-4.0
 ---
 
 # ComPile: A Large IR Dataset from Production Sources
 
 ## About
 
-Utilizing the LLVM compiler infrastructur shared by a number of languages, ComPile is a 182B token dataset of
+Utilizing the LLVM compiler infrastructur shared by a number of languages, ComPile is a large dataset of
 LLVM IR. The dataset is generated from programming languages built on the shared LLVM infrastructure, including Rust,
 Swift, Julia, and C/C++, by hooking into LLVM code generation either through the language's package manager or the
 compiler directly to extract the dataset of intermediate representations from production grade programs using our
-[dataset collection utility for the LLVM compilation infrastructure](https://github.com/llvm-ml/llvm-ir-dataset-utils).
-
+[dataset collection utility for the LLVM compilation infrastructure](https://doi.org/10.5281/zenodo.10155761).
 
 For an in-depth look at the statistical properties of dataset, please have a look at our [arXiv preprint](https://arxiv.org/abs/2309.15432).
 
+## Usage
+
+Using ComPile is relatively simple with HuggingFace's `datasets` library. To load the dataset, you can simply
+run the following in a Python interpreter or within a Python script:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset('llvm-ml/ComPile', split='train')
+```
+
+While this will just work, the download will take quite a while as `datasets` by default will download
+all 550GB+ within the dataset and cache it locally. Note that the data will be placed in the directory
+specified by the environment variable `HF_DATASETS_CACHE`, which defaults to `~/.cache/huggingface`.
+
+You can also load the dataset in a streaming format, where no data is saved locally:
+
+```python
+ds = load_dataset('llvm-ml/ComPile', split='train', streaming=True)
+```
+
+This makes experimentation much easier as no upfront large time investment is required, but is
+significantly slower than loading in the dataset from the local disk. For experimentation that
+requires more performance but might not require the whole dataset, you can also specify a portion
+of the dataset to download. For example, the following code will only download the first 10%
+of the dataset:
+
+```python
+ds = load_dataset('llvm-ml/ComPile', split='train[:10%]')
+```
+
+Once the dataset has been loaded, the individual module files can be accessed by iterating through
+the dataset or accessing specific indices:
+
+```python
+# We can iterate through the dataset
+next(iter(ds))
+# We can also access modules at specific indices
+ds[0]
+```
+
+Filtering and map operations can also be efficiently applied using primitives available within the
+HuggingFace `datasets` library. More documentation is available [here](https://huggingface.co/docs/datasets/index).
+
+## Dataset Format
+
+Each row in the dataset consists of an individual LLVM-IR Module along with some metadata. There are
+six columns associated with each row:
+
+1. `content` - This column contains the raw bitcode that composes the module. This can be written to a `.bc`
+file and manipulated using the standard llvm utilities or passed in directly through stdin if using something
+like Python's `subprocess`.
+2. `license_expression` - This column contains the SPDX expression describing the license of the project that the
+module came from.
+3. `license_source` - This column describes the way the `license_expression` was determined. This might indicate
+an individual package ecosystem (eg `spack`), license detection (eg `go_license_detector`), or might also indicate
+manual curation (`manual`).
+4. `license_files` - This column contains an array of license files. These file names map to licenses included in
+`/licenses/licenses-0.parquet`.
+5. `package_source` - This column contains information on the package that the module was sourced from. This is
+typically a link to a tar archive or git repository from which the project was built, but might also contain a
+mapping to a specific package ecosystem that provides the source, such as Spack.
+6. `language` - This column indicates the source language that the module was compiled from.
+
+## Licensing
+
+The individual modules within the dataset are subject to the licenses of the projects that they come from. License
+information is available in each row, including the SPDX license expression, the license files, and also a link to
+the package source where license information can be further validated.
 
-> Dataset release coming soon.
+The curation of these modules is licensed under a CC-BY-4.0 license.