Create README.md
This commit is contained in:
Prithiviraj Damodaran
huggingface-web
parent
165d869ef7
commit
e922b4d48f
|
|
@ -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.*
|
||||
|
||||
|
||||
Loading…
Reference in New Issue