Updated readme. Added link to model comparisons

README.md

@@ -78,6 +78,9 @@ _The file structure has been updated starting with version 0.6.0. This should on
 * `simpletransformers.question_answering` - Includes all Question Answering models.
   * `QuestionAnsweringModel`
 
+
+_[Back to Table of Contents](#table-of-contents)_
+
 ---
 
 ## Text Classification
@@ -275,6 +278,7 @@ print(raw_outputs)
 * [AG News Dataset - Multiclass Classification](https://medium.com/swlh/simple-transformers-multi-class-text-classification-with-bert-roberta-xlnet-xlm-and-8b585000ce3a?source=friends_link&sk=90e1c97255b65cedf4910a99041d9dfc)
 * [Toxic Comments Dataset - Multilabel Classification](https://towardsdatascience.com/multi-label-classification-using-bert-roberta-xlnet-xlm-and-distilbert-with-simple-transformers-b3e0cda12ce5?source=friends_link&sk=354e688fe238bfb43e9a575216816219)
 * [Semantic Textual Similarity Benchmark - Sentence Pair](https://medium.com/@chaturangarajapakshe/solving-sentence-pair-tasks-using-simple-transformers-2496fe79d616?source=friends_link&sk=fbf7439e9c31f7aefa1613d423a0fd40)
+* [AG News Dataset - BERT (base and distilled), RoBERTa (base and distilled), and XLNet compared](https://towardsdatascience.com/to-distil-or-not-to-distil-bert-roberta-and-xlnet-c777ad92f8?source=friends_link&sk=6a3c7940b18066ded94aeee95e354ed1)
 
 
 #### ClassificationModel
@@ -378,6 +382,8 @@ Returns:
 
 * wrong: List of InputExample objects corresponding to each incorrect prediction by the model  
 
+_[Back to Table of Contents](#table-of-contents)_
+
 ---
 
 ## Named Entity Recognition
@@ -524,6 +530,8 @@ Evaluates the model on eval_dataset.
 Converts a list of InputExample objects to a TensorDataset containing InputFeatures. Caches the InputFeatures.
 *Utility function for train() and eval() methods. Not intended to be used directly*
 
+_[Back to Table of Contents](#table-of-contents)_
+
 ___
 
 ## Question Answering
@@ -766,6 +774,8 @@ The maximum token length of an answer that can be generated.
 #### *null_score_diff_threshold: float*
 If null_score - best_non_null is greater than the threshold predict null.
 
+_[Back to Table of Contents](#table-of-contents)_
+
 ---
 
 ## Regression
@@ -837,6 +847,8 @@ model = ClassificationModel('roberta', 'roberta-base', args={'wandb_project': 'p
 
 For a complete example, see [here](https://medium.com/skilai/to-see-is-to-believe-visualizing-the-training-of-machine-learning-models-664ef3fe4f49).
 
+_[Back to Table of Contents](#table-of-contents)_
+
 ---
 
 ## Experimental Features
@@ -902,19 +914,24 @@ print(predictions)
 print(raw_outputs)
 ```
 
+_[Back to Table of Contents](#table-of-contents)_
+
 ---
 ## Loading Saved Models
 
 To load a saved model, provide the path to the directory containing the saved model as the `model_name`.
+_Note that you will need to specify the correct (usually the same used in training) `args` when loading the model_
 
 ```python
-model = ClassificationModel('roberta', 'outputs/')
+model = ClassificationModel('roberta', 'outputs/', args={})
 ```
 
 ```python
-model = NERModel('bert', 'outputs/')
+model = NERModel('bert', 'outputs/', args={})
 ```
 
+_[Back to Table of Contents](#table-of-contents)_
+
 ---
 
 
@@ -1091,6 +1108,8 @@ The `model_types` available for each task can be found under their respective se
 found in the Hugging Face docs should work. To use any of them set the correct `model_type` and `model_name` in the `args` 
 dictionary.
 
+_[Back to Table of Contents](#table-of-contents)_
+
 ---
 
 ## Acknowledgements