Update README.md

README.md

@@ -41,20 +41,20 @@ configs:
     path: data/train-*
 ---
 
-# Millions of bluesky posts
+# Five Million bluesky posts
 
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/674783a7c6317bfd72b33659/YSHpZ-KDe4v9yGnDt6c6Y.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/674783a7c6317bfd72b33659/bkAI0CJNPcZMrrP7VgGIC.png)
 
 <!-- Provide a quick summary of the dataset. -->
 
-This dataset contains millions public posts collected from Bluesky Social's firehose API, intended for machine learning research and experimentation with social media data.
+This dataset contains 5 million public posts collected from Bluesky Social's firehose API, intended for machine learning research and experimentation with social media data.
 
 This dataset was inspired by the Alpindales original 2 million posts dataset, this dataset expands on that dataset with much more data.
 
 Alpins dataset did not get author handles or image urls & metadata that was included in the posts. The images and their captions could potenically be invaluble for training so they have been collected.
 
-The is the largest version of the dataset with the goal being to reach a billion posts.
+This is the small version of the dataset to come for testing with formatting/smaller projects.
 
 This dataset is my own and is unaffiliated with bluesky or any potential employer.
 
@@ -78,13 +78,71 @@ The dataset could be used for:
 - Research on social media content moderation
 - Studying conversation structures and reply networks
 
-I have not been able to figure out how to parse the atproto image ref bytes into a image or blob url. I would appreciate a PR for that.
+
+### Loading dataset normally
+The dataset is meant to be downloaded with the huggingface load_dataset() function. From there you can either run the dataset as a iterable stream so you do not have to worry about memory or you can convert to a pandas dataframe.
+
+Note that you will need the to install the following libraries:
+```bash
+pip install pandas pyarrow datasets huggingface_hub
+```
+
+To download/load the huggingface dataset:
+```python
+from datasets import load_dataset
+dataset = load_dataset("Roronotalt/bluesky", split="train")
+```
+
+To pandas:
+```python
+new_dataset = dataset.to_pandas()
+```
+
+You can then save the pandas dataframe as a csv.
+
+Alternativley if you download the provided dataset parquet file in /data, you can convert the file to a csv using the following python code: 
+
+```bash
+python -c "import pandas as pd;
+df = http://pd.read_parquet('train-0000.parquet', engine='pyarrow');
+http://df.to_csv('output_file.csv', index=False) 
+"
+```
+Credit to @TyrantsMuse on twitter for the code snippet, @fr3fou for advice on compression, and @wavefnx for decoding the image bytes.
+
+### Loading the dataset images
+The dataset stores the bytes for a CID that can be used in conjuction with the author DID to get image blob URL from bluesky. The URL may not be valid.
+
+First you need the bluesky ATPROTO library:
+```bash
+pip install atproto
+```
+
+For this snippet it is assumed that you have already loaded the dataset thus, it is up to you to get the parts of the post mentioned.
+Then you can decode the image to a URL
+```python
+from atproto import CID
+
+# Image "blob", every dict in the embedded_array should have one
+encoded_string = image["blob"]
+# Post author DID, every post should have one
+author_did = post["author_did"]
+
+# I formatted a bit wrong so you have to fix formatting, whoops :p
+if encoded_string.startswith("b'") and encoded_string.endswith("'"):
+    encoded_string = encoded_string[2:-1]
+
+# Bluesky image blob URL
+url= f"https://bsky.social/xrpc/com.atproto.sync.getBlob?did={author_did}&cid={CID.decode(base64.b64decode(encoded_string))}"
+# Caption for image if one exists or empty string
+captions = image["alt"]
+```
 
 ## Dataset Curation
 
 The dataset not is filtered, sorting the dataset for quality or moderation may make it more valuable for your use cases. The dataset is as-is and no liablity is provided.
-There likley are duplicates, however deduping was done for each batch (1 million posts) so the amount is likley negligible.
 
+Deduping was done based on the post URIs. The dataset is sorted by the author column.
 ```Bibtex
 @article{roronotalt_bluesky,
   author = {Roronotalt},