Datasets:

stanfordnlp
/

SHP

@@ -137,9 +137,9 @@ Given a post P and two comments (A,B) we only included the preference A > B in t
 3. Neither comment was made by a deleted user, a moderator, or the post creator. The post was not made by a deleted user or moderator.
 4. The post has a score >= 10 and each comment has a score >= 2 (upvoted at least once).
-Reddit makes it very difficult to get anything beyond the top 1000 posts for subreddit.
-We started with the top-scoring 1000 posts (of all time) and searched for the 25 most similar posts to each one using the Reddit search function.
-By doing this recursively, we scraped up to 7500 post IDs for each subreddit and then used the AsyncPRAW API to scrape the top 50 comments from each post.
 We limited the scraping to 50 comments per post because the number of comments per post is Pareto-distributed, and we did not want a relatively small number of posts dominating the data.
 ### Preprocessing
@@ -160,9 +160,9 @@ If you want to finetune a model to predict human preferences (e.g., for NLG eval
    Although models like FLAN-T5 use positional embeddings, we found that the loss would not converge if we finetuned it on the entire input.
    To avoid this, truncate the post text (in the `history` field) as much as possible, such that the whole input is under 512 tokens (do not truncate the comment(s) however).
    If this is still over 512 tokens, simply skip the example.
-5. **Train for 1 epoch only**, as the [InstructGPT paper](https://arxiv.org/abs/2203.02155) paper suggests.
    Since the same comment appears in multiple preferences, it is easy to overfit to the data.
-6. **Train on less data.**
    Preferences with a large score ratio (e.g., comment A having 2x the score of comment B) will provide a stronger signal for finetuning the model, so you may only want to consider preferences above a certain `score_ratio`.
    The number of preferences per post is Pareto-distributed, so to prevent the model from over-fitting to certain posts, you may want to limit the number of preferences from a particular post.

 3. Neither comment was made by a deleted user, a moderator, or the post creator. The post was not made by a deleted user or moderator.
 4. The post has a score >= 10 and each comment has a score >= 2 (upvoted at least once).
+Reddit makes it very difficult to get anything beyond the top 1000 posts for each subreddit.
+We started with the top-scoring 1000 posts (of all time) and searched for the 25 most similar posts to each one using Reddit's search function.
+By doing this recursively, we scraped up to 7500 unique post IDs for each subreddit and then used the AsyncPRAW API to scrape the top 50 comments from each post.
 We limited the scraping to 50 comments per post because the number of comments per post is Pareto-distributed, and we did not want a relatively small number of posts dominating the data.
 ### Preprocessing
    Although models like FLAN-T5 use positional embeddings, we found that the loss would not converge if we finetuned it on the entire input.
    To avoid this, truncate the post text (in the `history` field) as much as possible, such that the whole input is under 512 tokens (do not truncate the comment(s) however).
    If this is still over 512 tokens, simply skip the example.
+4. **Train for 1 epoch only**, as the [InstructGPT paper](https://arxiv.org/abs/2203.02155) paper suggests.
    Since the same comment appears in multiple preferences, it is easy to overfit to the data.
+5. **Train on less data.**
    Preferences with a large score ratio (e.g., comment A having 2x the score of comment B) will provide a stronger signal for finetuning the model, so you may only want to consider preferences above a certain `score_ratio`.
    The number of preferences per post is Pareto-distributed, so to prevent the model from over-fitting to certain posts, you may want to limit the number of preferences from a particular post.