AutoNLP

AutoNLP: faster and easier training and deployments of SOTA NLP models

Installation

You can Install AutoNLP python package via PIP. Please note you will need python >= 3.7 for AutoNLP to work properly.

pip install autonlp

Quick start - in the terminal

Supported languages:

• English: en
• French: fr
• German: de
• Spanish: es
• Finnish: fi

Supported tasks:

• binary_classification
• multi_class_classification
• entity_extraction

First, create a project:

autonlp login --api-key YOUR_HUGGING_FACE_API_TOKEN
autonlp create_project --name sentiment_detection --language en --task binary_classification

Upload files and start the training. You need a training and a validation split. Only CSV files are supported at the moment.

# Train split
autonlp upload --project sentiment_detection --split train \
               --col_mapping review:text,sentiment:target \
               --files ~/datasets/train.csv
# Validation split
autonlp upload --project sentiment_detection --split valid \
               --col_mapping review:text,sentiment:target \
               --files ~/datasets/valid.csv

Once the files are uploaded, you can start training the model:

autonlp train --project sentiment_detection

Monitor the progress of your project.

# Project progress
autonlp project_info --name sentiment_detection
# Model metrics
autonlp metrics --model MODEL_ID

Quick start - Python API

Setting up:

from autonlp import AutoNLP
client = AutoNLP()
client.login(token="YOUR_HUGGING_FACE_API_TOKEN")

Creating a project and uploading files to it:

project = client.create_project(name="sentiment_detection", task="binary_classification", language="en")
project.upload(
    filepaths=["/path/to/train.csv"],
    split="train",
    col_mapping={
        "review": "text",
        "sentiment": "target",
    })

# also upload a validation with split="valid"

Start the training of your models:

project.train()

To monitor the progressn of your training:

project.refresh()
print(project)

After the training of your models has succeeded, you can retrieve its metrics and test it with the 🤗 Inference API:

client.predict(project="sentiment_detection", model_id=42, input_text="i love autonlp")

or use command line:

autonlp predict --project sentiment_detection --model_id 42 --sentence "i love autonlp"