added disclaimers to the readme

README.md

@@ -359,7 +359,7 @@ There isn't an effective way to validate the sentiment analysis models, as they
 
 The sentiment models that appear to work the best are the [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) and [siebert/sentiment-roberta-large-english](https://huggingface.co/siebert/sentiment-roberta-large-english) models, with few caveats. These two models generally perform very well at detecting sentiment in mid to long expressions. However the [siebert/sentiment-roberta-large-english](https://huggingface.co/siebert/sentiment-roberta-large-english) model tends to suffer when expressions are shorter and less complex in lexicon. Even the [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) model suffers from time to time if not enough context has been provided.
 
-The model that performed the worst is the [finiteautomata/beto-sentiment-analysis](https://huggingface.co/finiteautomata/beto-sentiment-analysis). This model seems to have the worst time trying to interpret meaning from sentences, even with strongly worded language such as "hate". For example, the expression "I hate you" returns a **NEUTRAL** response with 99.6% confidence, which differs from the [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) and [siebert/sentiment-roberta-large-english](https://huggingface.co/siebert/sentiment-roberta-large-english) models (**NEGATIVE**: ~96.5% - ~99.9% accuracy respectively). It appears that the [finiteautomata/beto-sentiment-analysis](https://huggingface.co/finiteautomata/beto-sentiment-analysis) gets confused when not enough context is provided. The expression "I hate you because you hurt my family" manages to return a **NEGATIVE** label, but with a mere 87.7% confidence. 
+The model that performed the worst is the [finiteautomata/beto-sentiment-analysis](https://huggingface.co/finiteautomata/beto-sentiment-analysis). This model seems to have the worst time trying to interpret meaning from sentences, even with strongly worded language such as "hate". For example, the expression "I hate you" returns a **NEUTRAL** response with 99.6% confidence, which differs from the [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) and [siebert/sentiment-roberta-large-english](https://huggingface.co/siebert/sentiment-roberta-large-english) models (**NEGATIVE**: ~96.5% - ~99.9% accuracy respectively). It appears that the [finiteautomata/beto-sentiment-analysis](https://huggingface.co/finiteautomata/beto-sentiment-analysis) gets confused when not enough context is provided. The expression "I hate you because you hurt my family" manages to return a **NEGATIVE** label, but with a mere 87.7% confidence. **NOTE**: _These expressions are purely meant for experimentation purposes, to convey model inaccuracies!_
 
 The unique model is the [bhadresh-savani/distilbert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion) model, which instead gives 6 general emotions as opposed to a binary **NEGATIVE** or **POSITIVE** rating: