Update README.md

README.md

@@ -10,7 +10,7 @@ pipeline_tag: feature-extraction
 <!-- Based on  https://huggingface.co/t5-small, model generates SQL from text given table list with "CREATE TABLE" statements. 
 This is a very light weigh model and could be used in multiple analytical applications. -->
 
-This model is an example on how to handle multi-target regression problem using llms. Model takes in tweet,stock ticker, month, last_price and volume for a stock (around the tweet was publish) and returns 1,2,3 and 7 day returns and 10 day anualized volatility.  Model uses feature vectors output by the tweet text (mobile-bert output), numerical (last price and volume), and categorical(stock ticker and month) sub-components then are concatenated into a single feature vector which is fed into a final ouput layers.
+This model is an example on how to handle multi-target regression problem using llms. Model takes in tweet,stock ticker, month, last_price and volume for a stock (around the tweet was publish) and returns 1,2,3 and 7 day returns and 10 day annualized volatility.  Model uses feature vectors output by the tweet text (mobile-bert output), numerical (last price and volume), and categorical(stock ticker and month) sub-components then are concatenated into a single feature vector which is fed into a final ouput layers.
 Used [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) for text feature extraction (MobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks). This model detects SQLInjection attacks in the input string (check How To Below). 
 This is again a very very light  model (100mb), used following dataset from [Kaggle](www.kaggle.com) called [Tweet Sentiment's Impact on Stock Returns (by THE DEVASTATOR)](https://www.kaggle.com/datasets/thedevastator/tweet-sentiment-s-impact-on-stock-returns).
 **Disclaimer: This model should not be used for trading. Data source is not verified, assumption is that data is synthetically generated. This is just an example how to handle multi-target regression problem**.
@@ -22,8 +22,8 @@ Contact us for more info: support@cloudsummary.com
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
-Model takes in tweet,stock ticker, month, last_price and volume for a stock (around the tweet was publish) and returns 1,2,3 and 7 day returns and 10 day anualized volatility.  Model uses feature vectors output by the tweet text (mobile-bert output), numerical (last price and volume), and categorical(stock ticker and month) sub-components then are concatenated into a single feature vector which is fed into a final ouput layers.
-
+Model takes in tweet,stock ticker, month, last_price and volume for a stock (around the tweet was publish) and returns 1,2,3 and 7 day returns and 10 day annualized volatility.  Model uses feature vectors output by the tweet text (mobile-bert output), numerical (last price and volume), and categorical(stock ticker and month) sub-components then are concatenated into a single feature vector which is fed into a final ouput layers.
+Model is trainded on 600k rows.
 
 
 - **Developed by:** cssupport (support@cloudsummary.com)