updated model to 250 subs

README.md

@@ -7,12 +7,12 @@ An NLP model that predicts subreddit based on the title of a post.
 
 ### Training
 
-DistilBERT is fine-tuned on [subreddit-posts](https://huggingface.co/datasets/daspartho/subreddit-posts), a dataset of titles of the top 1000 posts from the top 125 subreddits.
+DistilBERT is fine-tuned on [subreddit-posts](https://huggingface.co/datasets/daspartho/subreddit-posts), a dataset of titles of the top 1000 posts from the top 250 subreddits.
 
 For steps to make the model check out the [model](https://github.com/daspartho/predict-subreddit/blob/main/model.ipynb) notebook in the github repo or open in [Colab](https://colab.research.google.com/github/daspartho/predict-subreddit/blob/main/model.ipynb).
 
 ### Limitations and bias
 
-- Since the model is trained on top 125 subreddits ([for reference](http://redditlist.com/)) therefore it can only categorise within those subreddits.
+- Since the model is trained on top 250 subreddits ([for reference](http://redditlist.com/)) therefore it can only categorise within those subreddits.
 - Some subreddits have a specific format for their post title, like [r/todayilearned](https://www.reddit.com/r/todayilearned) where post title starts with "TIL" so the model becomes biased towards "TIL" --> r/todayilearned. This can be removed by cleaning the dataset of these specific terms.
 - In some subreddit like [r/gifs](https://www.reddit.com/r/gifs/), the title of the post doesn't matter much, so the model performs poorly on them.

config.json

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   "vocab_size": 30522
 }

pytorch_model.bin

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tokenizer.json

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