add snippet, fix citation

README.md

@@ -77,6 +77,69 @@ done
 A full set of scripts to recreate the dataset, including the quality signals, can be
 found [here](https://github.com/togethercomputer/RedPajama-Data).
 
+### Applying Filtering Rules
+You can use the quality signals to filter the raw RedPajama-V2 dataset for a given set of rules. For example, consider 
+the following set of rules used in Gopher:
+
+```python
+def gopher_rules_pass(sample) -> bool:
+    """ function returns True if the sample complies with Gopher rules """
+    signals = json.loads(sample["quality_signals"])
+
+    # rule 1: number of words between 50 and 10'000
+    word_count = signals["rps_doc_word_count"][0][2]
+    if word_count < 50 or word_count > 10_000:
+        return False
+
+    # rule 2: mean word length between 3 and 10
+    mean_word_length = signals["rps_doc_mean_word_length"][0][2]
+    if mean_word_length < 3 or mean_word_length > 10:
+        return False
+
+    # rule 2: symbol to word ratio below 0.1
+    symbol_word_ratio = signals["rps_doc_symbol_to_word_ratio"][0][2]
+    if  symbol_word_ratio > 0.1:
+        return False
+
+    # rule 3: 90% of lines need to start without a bullet point
+    n_lines = signals["ccnet_nlines"][0][2]
+    n_lines_bulletpoint_start = sum(map(lambda ln: ln[2], signals["rps_lines_start_with_bulletpoint"]))
+    if n_lines_bulletpoint_start / n_lines > 0.9:
+        return False
+
+    # rule 4: the ratio between characters in the most frequent 2-gram and the total number 
+    # of characters must be below 0.2
+    top_2_gram_frac = signals["rps_doc_frac_chars_top_2gram"][0][2]
+    if top_2_gram_frac > 0.2:
+        return False
+
+    # rule 5: ...
+    
+        
+    return True
+```
+
+Filtering the RedPajama-V2 dataset with this set of rules is then as easy as:
+
+```python
+ds_iterator = load_dataset(
+    "togethercomputer/RedPajama-Data-V2", 
+    snapshots=["2023-14"], 
+    languages=["en"], 
+    name="default", 
+    streaming=True
+)
+
+filtered_dataset = []
+
+for sample in ds_iterator["train"]:
+  
+  if not gopher_rules_pass(sample):
+    continue
+  
+  filtered_dataset.append(sample)
+```
+
 ### Dataset Summary
 
 RedPajama-V2 is an open dataset for training large laguage models and includes over 100B text documents. Out of these,
@@ -272,7 +335,7 @@ To cite RedPajama-V2, please use:
 ```
 @software{together2023redpajama-v2,
   author = {Together Computer},
-  title = {RedPajama-Data-v2: an Open Dataset for Training Large Language Models},
+  title = {RedPajama: an Open Dataset for Training Large Language Models},
   month = October,
   year = 2023,
   url = {https://github.com/togethercomputer/RedPajama-Data}