Update README.md

Added a number of images and filtering examples.

README.md

@@ -75,10 +75,10 @@ Our dataset is formatted in a Parquet data frame of the following structure:
 `label`: Fake/Real label. (1: Fake, 0: Real)
 
 ## Data splits
-`Systematic`: Systematically downloaded subset of the data (data downloaded from Hugging Face via automatic pipeline) \
-`Manual`: Manually downloaded subset of the data \
-`Commercial`: Commercial models subset \
-`PublicEval`: Evaluation set where generated images are paired with COCO or FFHQ for license-compliant redistribution. Note that these are not the "source" datasets used to sample the generated images. 
+`Systematic` (1,919,493 images): Systematically downloaded subset of the data (data downloaded from Hugging Face via automatic pipeline) \
+`Manual` (774,023 images): Manually downloaded subset of the data \
+`Commercial` (14,918 images): Commercial models subset \
+`PublicEval` (51,836 images): Evaluation set where generated images are paired with COCO or FFHQ for license-compliant redistribution. Note that these are not the "source" datasets used to sample the generated images
 
 ## Usage examples
 
@@ -104,7 +104,8 @@ for i, data in enumerate(commfor_train):
 *Note:*
 - Downloading and indexing the data can take some time, but only for the first time. **Downloading may use up to 2.2TB** (1.1TB data + 1.1TB re-indexed `arrow` files)
 - It is possible to randomly access data by passing an index (e.g., `commfor_train[10]`, `commfor_train[247]`).
-- It may be wise to set `cache_dir` to some other directory if your home directory is limited. By default, it will download data to `~/.cache/huggingface/datasets`. 
+- It may be wise to set `cache_dir` to some other directory if your home directory is limited. By default, it will download data to `~/.cache/huggingface/datasets`.
+- Not all images have a `prompt`. This can be because the generator does not require text prompts (e.g., unconditional, class-conditional) or due to an error. In cases where you need a specific portion of data, you can use the `.filter()` method (e.g., for data with prompts, `commfor_train.filter(lambda x: x['prompt'] != "", num_proc=8)`)
 
 It is also possible to use streaming for some use cases (e.g., downloading only a certain subset or a small portion of data).
 ```python