Update README.md

README.md

@@ -25,10 +25,11 @@ SteamSHP-XL is a preference model trained to predict human preferences, given so
 It can be used for NLG evaluation or to train a smaller reward model for RLHF.
 
 It is a FLAN-T5-xl model (3B parameters) finetuned on:
-1. The [Stanford Human Preferences Dataset (SHP)](https://huggingface.co/datasets/stanfordnlp/SHP), which contains aggregate human preferences sourced from 18 different communities on Reddit (e.g., `askculinary`, `legaladvice`, etc.).
+1. The [Stanford Human Preferences Dataset (SHP)](https://huggingface.co/datasets/stanfordnlp/SHP), which contains collective human preferences sourced from 18 different communities on Reddit (e.g., `askculinary`, `legaladvice`, etc.).
 2. The helpfulness data in [Anthropic's HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf) dataset.
 
-There is a smaller variant called [SteamSHP-Large](https://huggingface.co/kawine/SteamSHP-flan-t5-large) that was made by finetuning FLAN-T5-large (780M parameters), which is 0.75 percentage points less accurate on the test data.
+There is a smaller variant called [SteamSHP-Large](https://huggingface.co/kawine/SteamSHP-flan-t5-large) that was made by finetuning FLAN-T5-large (780M parameters).
+Despite being 1/4 of the size, it is on average only 0.75 points less accurate on the SHP + Anthropic test data (across all domains).
 
 
 ## Usage
@@ -52,7 +53,7 @@ Here's how to use the model:
 ```python
 
 >> from transformers import T5ForConditionalGeneration, T5Tokenizer
->> device = 'cuda'
+>> device = 'cuda' # if you have a GPU
 
 >> tokenizer = T5Tokenizer.from_pretrained('stanfordnlp/SteamSHP-flan-t5-xl')
 >> model = T5ForConditionalGeneration.from_pretrained('stanfordnlp/SteamSHP-flan-t5-xl').to(device)
@@ -112,7 +113,7 @@ Biases in the datasets used to train SteamSHP-XL may be propagated downstream to
 Although SHP filtered out posts with NSFW (over 18) content, chose subreddits that were well-moderated and had policies against harassment and bigotry, some of the data may contain discriminatory or harmful language.
 Reddit users on the subreddits covered by SHP are also not representative of the broader population. They are disproportionately from developed, Western, and English-speaking countries. 
 
-It is also worth noting that the more preferred response in SHP or HH-RLHF is not necessarily the more correct one -- the data just reflects the aggregate preference of Reddit users (in SHP's case) and individuals' preferences (in HH-RLHF's case).
+It is also worth noting that the more preferred response in SHP or HH-RLHF is not necessarily the more correct one -- the data just reflects the collective preference of Reddit users (in SHP's case) and individual preferences (in HH-RLHF's case).
 [Past work](https://www.anthropic.com/model-written-evals.pdf) by Anthropic has found that models optimized for human preference can be obsequious, at the expense of the truth.