Create README.md

README.md

@@ -0,0 +1,95 @@
+# Parrot
+
+## 1. What is Parrot?
+Parrot is a paraphrase based utterance augmentation framework purpose built to accelerate training NLU models. A paraphrase framework is more than just a paraphrasing model.
+
+## 2. Why Parrot?
+**Huggingface** lists [12 paraphrase models,](https://huggingface.co/models?pipeline_tag=text2text-generation&search=paraphrase)  **RapidAPI** lists 7 fremium and commercial paraphrasers like [QuillBot](https://rapidapi.com/search/paraphrase?section=apis&page=1), Rasa has discussed an experimental paraphraser for augmenting text data [here](https://forum.rasa.com/t/paraphrasing-for-nlu-data-augmentation-experimental/27744), Sentence-transfomers offers a [paraphrase mining utility](https://www.sbert.net/examples/applications/paraphrase-mining/README.html) and [NLPAug](https://github.com/makcedward/nlpaug) offers word level augmentation with a [PPDB](http://paraphrase.org/#/download) (a multi-million paraphrase database). While these attempts at paraphrasing are great, there are still some gaps and paraphrasing is NOT yet a mainstream option for text augmentation in building NLU models....Parrot is a humble attempt to fill some of these gaps.
+
+**What is a good paraphrase?** Almost all conditioned text generation models are validated  on 2 factors, (1) if the generated text conveys the same meaning as the original context (Adequacy) (2) if the text is fluent / grammatically correct english (Fluency). For instance Neural Machine Translation outputs are tested for Adequacy and Fluency. But [a good paraphrase](https://www.aclweb.org/anthology/D10-1090.pdf) should be adequate and fluent while being as different as possible on the surface lexical form. With respect to this definition, the  **3 key metrics** that measures the quality of paraphrases are:
+ - **Adequacy** (Is the meaning preserved adequately?) 
+ - **Fluency** (Is the paraphrase fluent English?) 
+ - **Diversity (Lexical / Phrasal / Syntactical)** (How much has the paraphrase changed the original sentence?)
+
+*Parrot offers knobs to control Adequacy, Fluency and Diversity as per your needs.*
+
+**What makes a paraphraser a good augmentor?** For training a NLU model we just don't need a lot of utterances but utterances with intents and slots/entities annotated. Typical flow would be:
+- Given an **input utterance  + input annotations** a good augmentor spits out N **output paraphrases** while preserving the intent and slots. 
+ - The output paraphrases are then converted into annotated data using the input annotations that we got in step 1.
+ - The annotated data created out of the output paraphrases then makes the training dataset for your NLU model.
+
+But in general being a generative model paraphrasers doesn't guarantee to preserve the slots/entities. So the ability to generate high quality paraphrases in a constrained fashion without trading off the intents and slots for lexical dissimilarity makes a paraphraser a good augmentor. *More on this in section 3 below*
+
+### Installation
+```python
+pip install parrot
+```
+
+### Quickstart
+```python
+
+import warnings
+warnings.filterwarnings("ignore")
+parrot = Parrot(model_tag="prithivida/parrot_paraphraser_on_T5", use_gpu=True)
+phrases = ["Can you recommed some upscale restaurants in Rome?",
+           "What are the famous places we should not miss in Russia?"
+]
+
+for phrase in phrases:
+  print("-"*100)
+  print(phrase)
+  print("-"*100)
+  para_phrases = parrot.augment(input_phrase=phrase)
+  for para_phrase in para_phrases:
+   print(para_phrase)
+```
+
+<pre>
+
+
+-----------------------------------------------------------------------------
+Input_phrase: Can you recommed some upscale restaurants in Rome?
+-----------------------------------------------------------------------------
+"which upscale restaurants are recommended in rome?"
+"which are the best restaurants in rome?"
+"are there any upscale restaurants near rome?"
+"can you recommend a good restaurant in rome?"
+"can you recommend some of the best restaurants in rome?"
+"can you recommend some best restaurants in rome?"
+"can you recommend some upscale restaurants in rome?"
+-----------------------------------------------------------------------------
+Input_phrase: What are the famous places we should not miss in Russia
+-----------------------------------------------------------------------------
+"which are the must do places for tourists to visit in russia?"
+"what are the best places to visit in russia?"
+"what are some of the most visited sights in russia?"
+"what are some of the most beautiful places in russia that tourists should not miss?"
+"which are some of the most beautiful places to visit in russia?"
+"what are some of the most important places to visit in russia?"
+"what are some of the most famous places of russia?"
+"what are some places we should not miss in russia?"
+
+</pre>
+
+### Knobs
+
+```python
+
+ para_phrases = parrot.augment(input_phrase=phrase, 
+                               diversity_ranker="levenshtein",
+                               do_diverse=False, 
+                               max_return_phrases = 10, 
+                               max_length=32, 
+                               adequacy_threshold = 0.99, 
+                               fluency_threshold = 0.90)
+
+```
+
+
+## 3. Scope
+
+In the space of conversational engines, knowledge bots are to which **we ask questions** like *"when was the Berlin wall teared down?"*, transactional bots are to which **we give commands** like *"Turn on the music please"* and voice assistants are the ones which can do both answer questions and action our commands. Parrot mainly foucses on augmenting texts typed-into or spoken-to conversational interfaces for building robust NLU models. (*So usually people neither type out or yell out long paragraphs to conversational interfaces. Hence the pre-trained model is trained  on text samples of maximum length of 64.*)
+
+*While Parrot predominantly aims to be a text augmentor for building good NLU models, it can also be used as a pure-play paraphraser.*
+
+