README update

README.md

@@ -58,13 +58,13 @@ your@user:/your/project/path$ pyss3
 (pyss3) >>> load my_model
 (pyss3) >>> grid_search path/to/dataset 4-fold -s r(.2,.8,6) -l r(.1,2,6) -p r(.5,2,6)
 ```
-In this illustrative example, `s` will take 6 different values between .2 and .8, `l` between .1 and 2, and `p` between .5 and 2. After the grid search finishes, we can use the following command to open up the interactive plot in the browser:
+In this illustrative example, `s` will take 6 different values between .2 and .8, `l` between .1 and 2, and `p` between .5 and 2. After the grid search finishes, we can use the following command to open up an interactive 3D plot in the browser:
 ```console
 (pyss3) >>> plot evaluations
 ```
 ![img](https://raw.githubusercontent.com/sergioburdisso/pyss3/master/docs/_static/plot_evaluations.gif)
 
-Each dot represents an experiment/evaluation performed using that particular combination of values (s, l, and p). Also, dots are painted proportional to how good the performance was using that configuration of the model. Researchers can interactively change the evaluation metrics to be used (accuracy, precision, recall, f1, etc.) and plots will update "on the fly". Additionally, when the cursor is moved over a data point, useful information is shown (including a "compact" representation of the confusion matrix obtained in that experiment). Finally, it is worth mentioning that, before showing the 3D plots, PySS3 creates and save a single and portable HTML file in your project folder containing the interactive plots. This allows researchers to store, send or upload the plots to another place using this single HTML file (or even provide a link to this file in their own papers, which would be nicer for readers, plus it would increase experimentation transparency). For example, we have uploaded two of these files we've obtained for the "Tutorials" section: ["Movie Review Classification"](http://tworld.io/ss3/ss3_model_evaluation[movie_review_3grams].html) and ["Topic Categorization"](http://tworld.io/ss3/ss3_model_evaluation[topics_3grams].html) evaluation plots.
+Each dot represents an experiment/evaluation performed using that particular combination of values (s, l, and p). Also, dots are painted proportional to how good the performance was using that configuration of the model. Researchers can interactively change the evaluation metrics to be used (accuracy, precision, recall, f1, etc.) and plots will update "on the fly". Additionally, when the cursor is moved over a data point, useful information is shown (including a "compact" representation of the confusion matrix obtained in that experiment). Finally, it is worth mentioning that, before showing the 3D plots, PySS3 creates and save a single and portable HTML file in your project folder containing the interactive plots. This allows researchers to store, send or upload the plots to another place using this single HTML file (or even provide a link to this file in their own papers, which would be nicer for readers, plus it would increase experimentation transparency). For example, we have uploaded two of these files for you to see: ["Movie Review Classification"](http://tworld.io/ss3/ss3_model_evaluation[movie_review_3grams].html) and ["Topic Categorization"](http://tworld.io/ss3/ss3_model_evaluation[topics_3grams].html) evaluation plots (Note: these two plots were obtained following the tutorials given below).
 
 
 ## The PySS3 Workflow