bramalkema / nlpcloud-php

NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, paraphrasing, intent classification, product description and ad generation, chatbot, grammar and spelling correction, keywords and keyphrases extraction, text generation, image generation, code generation, blog post generation,

PHP Client For NLP Cloud

This is the PHP client for the NLP Cloud API. See the documentation for more details.

NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, dialogue summarization, paraphrasing, intent classification, product description and ad generation, chatbot, grammar and spelling correction, keywords and keyphrases extraction, text generation, image generation, blog post generation, code generation, question answering, automatic speech recognition, machine translation, language detection, semantic search, semantic similarity, tokenization, POS tagging, embeddings, and dependency parsing. It is ready for production, served through a REST API.

You can either use the NLP Cloud pre-trained models, fine-tune your own models, or deploy your own models.

If you face an issue, don't hesitate to raise it as a Github issue. Thanks!

Installation

Install via composer.

Create a composer.json file containing at least the following:

{
    "require": {
        "nlpcloud/nlpcloud-client": "*"
    }
}

Then launch the following:

composer install

Examples

Here is a full example that summarizes a text using Facebook's Bart Large CNN model, with a fake token:

require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('bart-large-cnn','4eC39HqLyjWDarjtT1zdp7dc');
echo json_encode($client->summarization('One month after the United States began what has become a 
  troubled rollout of a national COVID vaccination campaign, the effort is finally 
  gathering real steam. Close to a million doses -- over 951,000, to be more exact -- 
  made their way into the arms of Americans in the past 24 hours, the U.S. Centers 
  for Disease Control and Prevention reported Wednesday. That s the largest number 
  of shots given in one day since the rollout began and a big jump from the 
  previous day, when just under 340,000 doses were given, CBS News reported. 
  That number is likely to jump quickly after the federal government on Tuesday 
  gave states the OK to vaccinate anyone over 65 and said it would release all 
  the doses of vaccine it has available for distribution. Meanwhile, a number 
  of states have now opened mass vaccination sites in an effort to get larger 
  numbers of people inoculated, CBS News reported.'));

Here is a full example that does the same thing, but on a GPU:

require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('bart-large-cnn','4eC39HqLyjWDarjtT1zdp7dc', True);
echo json_encode($client->summarization('One month after the United States began what has become a 
  troubled rollout of a national COVID vaccination campaign, the effort is finally 
  gathering real steam. Close to a million doses -- over 951,000, to be more exact -- 
  made their way into the arms of Americans in the past 24 hours, the U.S. Centers 
  for Disease Control and Prevention reported Wednesday. That s the largest number 
  of shots given in one day since the rollout began and a big jump from the 
  previous day, when just under 340,000 doses were given, CBS News reported. 
  That number is likely to jump quickly after the federal government on Tuesday 
  gave states the OK to vaccinate anyone over 65 and said it would release all 
  the doses of vaccine it has available for distribution. Meanwhile, a number 
  of states have now opened mass vaccination sites in an effort to get larger 
  numbers of people inoculated, CBS News reported.'));

Here is a full example that does the same thing, but on a French text:

require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('bart-large-cnn','4eC39HqLyjWDarjtT1zdp7dc', True, 'fr');
echo json_encode($client->summarization('Sur des images aériennes, prises la veille par un vol de surveillance 
  de la Nouvelle-Zélande, la côte d’une île est bordée d’arbres passés du vert 
  au gris sous l’effet des retombées volcaniques. On y voit aussi des immeubles
  endommagés côtoyer des bâtiments intacts. « D’après le peu d’informations
  dont nous disposons, l’échelle de la dévastation pourrait être immense, 
  spécialement pour les îles les plus isolées », avait déclaré plus tôt 
  Katie Greenwood, de la Fédération internationale des sociétés de la Croix-Rouge.
  Selon l’Organisation mondiale de la santé (OMS), une centaine de maisons ont
  été endommagées, dont cinquante ont été détruites sur l’île principale de
  Tonga, Tongatapu. La police locale, citée par les autorités néo-zélandaises,
  a également fait état de deux morts, dont une Britannique âgée de 50 ans,
  Angela Glover, emportée par le tsunami après avoir essayé de sauver les chiens
  de son refuge, selon sa famille.'));

A json object is returned:

{
  "summary_text": "Over 951,000 doses were given in the past 24 hours. That's the largest number of shots given in one day since the  rollout began. That number is likely to jump quickly after the federal government gave states the OK to vaccinate anyone over 65. A number of states have now opened mass vaccination sites."
}

Usage

Client Initialization

Pass the model you want to use and the NLP Cloud token to the client during initialization.

The model can either be a pretrained model like en_core_web_lg, bart-large-mnli... but also one of your custom models, using custom_model/<model id> (e.g. custom_model/2568).

Your token can be retrieved from your NLP Cloud dashboard.

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('<model>','<your token>');

If you want to use a GPU, pass true as a 3rd argument.

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('<model>','<your token>', true);

If you want to use the multilingual add-on in order to process non-English texts, set '<your language code>' as a 4th argument. For example, if you want to process French text, you should set 'fr'.

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('<model>','<your token>', false, '<your language code>');

If you want to make asynchronous requests, pass true as a 4th argument.

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud("<model>", "<your token>", false, '<your language code>', true);

If you are making asynchronous requests, you will always receive a quick response containing a URL. You should then poll this URL with async_result() on a regular basis (every 10 seconds for example) in order to check if the result is available. Here is an example:

client.asyncResult("https://api.nlpcloud.io/v1/get-async-result/21718218-42e8-4be9-a67f-b7e18e03b436")

The above command returns an object.

Ad Generation And Product Description Endpoint

Call the adGeneration() method and pass a list of keywords you want to generate you product description or ad from.

echo json_encode($client->adGeneration(["Keyword 1", "Keyword 2", "Keyword 3", ...]))

The above command returns an object.

Automatic Speech Recognition (Speech to Text) Endpoint

Call the asr() method and pass the url that hosts your audio or video file.

echo json_encode($client->asr("your url"))

The above command returns an object.

Article Generation Endpoint

Call the articleGeneration() method and pass the title of the blog post your want to generate:

echo json_encode($client.articleGeneration("<Your title>"))

The above command returns an object.

Chatbot Endpoint

Call the chatbot() method and pass your input. As an option, you can also pass a context and a conversation history that is an array of named arrays. Each named array is made of an input and a response from the chatbot.

echo json_encode($client.chatbot("Your input", "context", [["input"=>"input 1","response"=>"response 1"], ["input"=>"input 2","response"=>"response 2"], ...]))

The above command returns an object.

Classification Endpoint

Call the classification() method and pass 3 arguments:

1. The text you want to classify, as a string
2. The candidate labels for your text, as an array of strings
3. Whether the classification should be multi-class or not, as a boolean

echo json_encode($client->classification("<Your block of text>", ["label 1", "label 2", ...], True|False))

The above command returns an object.

Code Generation Endpoint

Call the codeGeneration() method and pass the description of your program:

echo json_encode($client.codeGeneration("<Your instruction>"))

The above command returns an object.

Dependencies Endpoint

Call the dependencies() method and pass the text you want to perform part of speech tagging (POS) + arcs on.

echo json_encode($client->dependencies("<Your block of text>"))

The above command returns an object.

Embeddings Endpoint

Call the embeddings() method and pass an array of blocks of text that you want to extract embeddings from.

echo json_encode($client->embeddings(array("<Text 1>", "<Text 2>", "<Text 3>", ...)))

The above command returns an object.

Entities Endpoint

Call the entities() method and pass the text you want to perform named entity recognition (NER) on.

echo json_encode($client->entities('<Your block of text>'))

The above command returns an object.

Generation Endpoint

Call the generation() method and pass the following arguments:

1. The block of text that starts the generated text. 256 tokens maximum for GPT-J on CPU, 1024 tokens maximum for GPT-J and GPT-NeoX 20B on GPU, and 2048 tokens maximum for Fast GPT-J and Finetuned GPT-NeoX 20B on GPU.
2. (Optional) min_length: The minimum number of tokens that the generated text should contain. 256 tokens maximum for GPT-J on CPU, 1024 tokens maximum for GPT-J and GPT-NeoX 20B on GPU, and 2048 tokens maximum for Fast GPT-J and Finetuned GPT-NeoX 20B on GPU.. If length_no_input is false, the size of the generated text is the difference between min_length and the length of your input text. If length_no_input is true, the size of the generated text simply is min_length. Defaults to 10.
3. (Optional) max_length: Optional. The maximum number of tokens that the generated text should contain. 256 tokens maximum for GPT-J on CPU, 1024 tokens maximum for GPT-J and GPT-NeoX 20B on GPU, and 2048 tokens maximum for Fast GPT-J and Finetuned GPT-NeoX 20B on GPU. If length_no_input is false, the size of the generated text is the difference between max_length and the length of your input text. If length_no_input is true, the size of the generated text simply is max_length. Defaults to 50.
4. (Optional) length_no_input: Whether min_length and max_length should not include the length of the input text, as a boolean. If false, min_length and max_length include the length of the input text. If true, min_length and max_length don't include the length of the input text. Defaults to false.
5. (Optional) end_sequence: A specific token that should be the end of the generated sequence, as a string. For example if could be . or \n or ### or anything else below 10 characters.
6. (Optional) remove_input: Whether you want to remove the input text form the result, as a boolean. Defaults to false.
7. (Optional) do_sample: Whether or not to use sampling ; use greedy decoding otherwise, as a boolean. Defaults to true.
8. (Optional) num_beams: Number of beams for beam search. 1 means no beam search. This is an integer. Defaults to 1.
9. (Optional) early_stopping: Whether to stop the beam search when at least num_beams sentences are finished per batch or not, as a boolean. Defaults to false.
10. (Optional) no_repeat_ngram_size: If set to int > 0, all ngrams of that size can only occur once. This is an integer. Defaults to 0.
11. (Optional) num_return_sequences: The number of independently computed returned sequences for each element in the batch, as an integer. Defaults to 1.
12. (Optional) top_k: The number of highest probability vocabulary tokens to keep for top-k-filtering, as an integer. Maximum 1000 tokens. Defaults to 0.
13. (Optional) top_p: If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation. This is a float. Should be between 0 and 1. Defaults to 0.7.
14. (Optional) temperature: The value used to module the next token probabilities, as a float. Should be between 0 and 1. Defaults to 1.
15. (Optional) repetition_penalty: The parameter for repetition penalty, as a float. 1.0 means no penalty. Defaults to 1.0.
16. (Optional) length_penalty: Exponential penalty to the length, as a float. 1.0 means no penalty. Set to values < 1.0 in order to encourage the model to generate shorter sequences, or to a value > 1.0 in order to encourage the model to produce longer sequences. Defaults to 1.0.
17. (Optional) bad_words: List of tokens that are not allowed to be generated, as a list of strings. Defaults to null.
18. (Optional) remove_end_sequence: Optional. Whether you want to remove the end_sequence string from the result. Defaults to false.

echo json_encode($client->generation("<Your input text>"))

Grammar and Spelling Correction Endpoint

Call the gsCorrection() method and pass the text you want correct:

echo json_encode($client.gsCorrection("<Your block of text>"))

The above command returns an object.

Image Generation Endpoint

Call the imageGeneration() method and pass the text you want to use to generate your image:

echo json_encode($client.imageGeneration("<Your block of text>"))

The above command returns an object.

Intent Classification Endpoint

Call the intentClassification() method and pass the text you want to extract intents from:

echo json_encode($client.intentClassification("<Your block of text>"))

The above command returns an object.

Keywords and Keyphrases Extraction Endpoint

Call the kwKpExtraction() method and pass the text you want to extract keywords and keyphrases from:

echo json_encode($client.kwKpExtraction("<Your block of text>"))

The above command returns an object.

Language Detection Endpoint

Call the langdetection() method and pass the text you want to analyze.

echo json_encode($client->langdetection("<Text to analyze>"))

The above command returns an object.

Library Versions Endpoint

Call the libVersions() method to know the versions of the libraries used behind the hood with the model (for example the PyTorch, TensorFlow, and spaCy version used).

echo json_encode($client->libVersions())

The above command returns an object.

Paraphrasing Endpoint

Call the paraphrasing() method and pass the text you want to paraphrase.

echo json_encode($client->summarization("<Your text to paraphrase>"))

The above command returns an object.

Semantic Search Endpoint

Call the semanticSearch() method and pass your search query:

echo json_encode($client->semanticSearch("<Your search query>"))

The above command returns an object.

Semantic Similarity Endpoint

Call the semanticSimilarity() method and pass an array made up of 2 blocks of text that you want to compare.

echo json_encode($client->semanticSimilarity(array("<Block of text 1>", "<Block of text 2>")))

The above command returns an object.

Sentence Dependencies Endpoint

Call the sentenceDependencies() method and pass a block of text made up of several sentencies you want to perform POS + arcs on.

echo json_encode($client->sentenceDependencies("<Your block of text>"))

The above command returns an object.

Sentiment Analysis Endpoint

Call the sentiment() method and pass the text you want to analyze the sentiment of:

echo json_encode($client->sentiment("<Your block of text>"))

The above command returns an object.

Question Answering Endpoint

Call the question() method and pass the following:

1. Your question
2. A context that the model will use to try to answer your question

echo json_encode($client->question("<Your question>","<Your context>"))

The above command returns an object.

Summarization Endpoint

Call the summarization() method and pass the text you want to summarize.

echo json_encode($client->summarization("<Your text to summarize>"))

The above command returns an object.

Tokenization Endpoint

Call the tokens() method and pass the text you want to tokenize.

echo json_encode($client->tokens("<Your block of text>"))

The above command returns an object.

Translation Endpoint

Call the translation() method and pass the text you want to translate.

echo json_encode($client->translation("<Your text to translate>"))

The above command returns an object.