To help you get started, we've collected the most common ways that simpletransformers is being used within popular public projects.

ThilinaRajapakse / simpletransformers / simpletransformers / ner / ner_model.py View on Github

to_predict: A python list of text (str) to be sent to the model for prediction.

        Returns:
            preds: A Python list of lists with dicts containg each word mapped to its NER tag.
            model_outputs: A python list of the raw model outputs for each text.
        """

        tokenizer = self.tokenizer
        device = self.device
        model = self.model
        args = self.args
        pad_token_label_id = self.pad_token_label_id

        self._move_model_to_device()

        predict_examples = [InputExample(i, sentence.split(), ["O" for word in sentence.split()]) for i, sentence in enumerate(to_predict)]

        eval_dataset = self.load_and_cache_examples(None, to_predict=predict_examples)

        eval_sampler = SequentialSampler(eval_dataset)
        eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args["eval_batch_size"])

        eval_loss = 0.0
        nb_eval_steps = 0
        preds = None
        out_label_ids = None
        model.eval()

        for batch in tqdm(eval_dataloader, disable=args['silent']):
            batch = tuple(t.to(device) for t in batch)

            with torch.no_grad():

View more ways to use simpletransformers

Simple Transformers

This library is based on the Transformers library by HuggingFace. Simple Transformers lets you quickly train and evaluate Transformer models. Only 3 lines of code are needed to initialize, train, and evaluate a model.

Supported Tasks:

• Information Retrieval (Dense Retrieval)
• (Large) Language Models (Training, Fine-tuning, and Generation)
• Encoder Model Training and Fine-tuning
• Sequence Classification
• Token Classification (NER)
• Question Answering
• Language Generation
• T5 Model
• Seq2Seq Tasks
• Multi-Modal Classification
• Conversational AI.

Table of contents

• Simple Transformers
• Table of contents
  • Setup
    • With Conda
      • Optional
  • Usage
    • A quick example
    • Experiment Tracking with Weights and Biases
  • Current Pretrained Models
  • Contributors ✨
  • How to Contribute
    • How to Update Docs
  • Acknowledgements

Setup

With Conda

1. Install Anaconda or Miniconda Package Manager from here
2. Create a new virtual environment and install packages.

$ conda create -n st python pandas tqdm
$ conda activate st

Using Cuda:

$ conda install pytorch>=1.6 cudatoolkit=11.0 -c pytorch

Without using Cuda

$ conda install pytorch cpuonly -c pytorch

3. Install simpletransformers.

$ pip install simpletransformers

Optional

1. Install Weights and Biases (wandb) for tracking and visualizing training in a web browser.

$ pip install wandb

Usage

All documentation is now live at simpletransformers.ai

Simple Transformer models are built with a particular Natural Language Processing (NLP) task in mind. Each such model comes equipped with features and functionality designed to best fit the task that they are intended to perform. The high-level process of using Simple Transformers models follows the same pattern.

1. Initialize a task-specific model
2. Train the model with train_model()
3. Evaluate the model with eval_model()
4. Make predictions on (unlabelled) data with predict()

However, there are necessary differences between the different models to ensure that they are well suited for their intended task. The key differences will typically be the differences in input/output data formats and any task specific features/configuration options. These can all be found in the documentation section for each task.

The currently implemented task-specific Simple Transformer models, along with their task, are given below.

• Please refer to the relevant section in the docs for more information on how to use these models.
• Example scripts can be found in the examples directory.
• See the Changelog for up-to-date changes to the project.

A quick example

from simpletransformers.classification import ClassificationModel, ClassificationArgs
import pandas as pd
import logging


logging.basicConfig(level=logging.INFO)
transformers_logger = logging.getLogger("transformers")
transformers_logger.setLevel(logging.WARNING)

# Preparing train data
train_data = [
    ["Aragorn was the heir of Isildur", 1],
    ["Frodo was the heir of Isildur", 0],
]
train_df = pd.DataFrame(train_data)
train_df.columns = ["text", "labels"]

# Preparing eval data
eval_data = [
    ["Theoden was the king of Rohan", 1],
    ["Merry was the king of Rohan", 0],
]
eval_df = pd.DataFrame(eval_data)
eval_df.columns = ["text", "labels"]

# Optional model configuration
model_args = ClassificationArgs(num_train_epochs=1)

# Create a ClassificationModel
model = ClassificationModel(
    "roberta", "roberta-base", args=model_args
)

# Train the model
model.train_model(train_df)

# Evaluate the model
result, model_outputs, wrong_predictions = model.eval_model(eval_df)

# Make predictions with the model
predictions, raw_outputs = model.predict(["Sam was a Wizard"])

Experiment Tracking with Weights and Biases

• Weights and Biases makes it incredibly easy to keep track of all your experiments. Check it out on Colab here:

Current Pretrained Models

For a list of pretrained models, see Hugging Face docs.

The model_types available for each task can be found under their respective section. Any pretrained model of that type 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.

Contributors ✨

Thanks goes to these wonderful people (emoji key):

hawktang 💻	Mabu Manaileng 💻	Ali Hamdi Ali Fadel 💻	Tovly Deutsch 💻	hlo-world 💻	huntertl 💻	Yann Defretin 💻 📖 💬 🤔
Manuel 📖 💻	Gilles Jacobs 📖	shasha79 💻	Mercedes Garcia 💻	Hammad Hassan Tarar 💻 📖	Todd Cook 💻	Knut O. Hellan 💻 📖
nagenshukla 💻	flaviussn 💻 📖	Marc Torrellas 🚧	Adrien Renaud 💻	jacky18008 💻	Matteo Senese 💻	sarthakTUM 📖 💻
djstrong 💻	Hyeongchan Kim 📖	Pradhy729 💻 🚧	Iknoor Singh 📖	Gabriel Altay 💻	flozi00 📖 💻 🚧	alexysdussier 💻
Jean-Louis Queguiner 📖	aced125 💻	Laksh1997 💻	Changlin_NLP 💻	jpotoniec 💻	fcggamou 💻 📖	guy-mor 🐛 💻
Cahya Wirawan 💻	BjarkePedersen 💻	tekkkon 💻	Amit Garg 💻	caprone 🐛	Ather Fawaz 💻	Santiago Castro 📖
taranais 💻	Pablo N. Marino 💻 📖	Anton Kiselev 💻 📖	Alex 💻	Karthik Ganesan 💻	Zhylko Dima 💻	Jonatan Kłosko 💻
sarapapi 💻 💬	Abdul 💻	James Milliman 📖	Suraj Parmar 📖	KwanHong Lee 💬	Erik Fäßler 💻	Thomas Søvik 💬
Gagandeep Singh 💻 📖	Andrea Esuli 💻	DM2493 💻	Nick Doiron 💻	Abhinav Gupta 💻	Martin H. Normark 📖	Mossad Helali 💻
calebchiam 💻	Daniele Sartiano 💻	tuner007 📖	xia jiang 💻	Hendrik Buschmeier 📖	Mana Borwornpadungkitti 📖	rayline 💻
Mehdi Heidari 💻	William Roe 💻	Álvaro Abella Bascarán 💻	Brett Fazio 📖	Viet-Tien 💻	Bisola Olasehinde 💻 📖	William Chen 📖
Reza Ebrahimi 📖	gabriben 📖	Prashanth Kurella 💻	dopc 💻	Tanish Tyagi 📖 💻	kongyurui 💻	Andrew Lensen 💻
jinschoi 💻	Le Nguyen Khang 💻	Jordi Mas 📖	mxa 💻	MichelBartels 💻	Luke Tudge 📖	Saint 💻
deltaxrg 💻 📖	Fortune Adekogbe 💻

This project follows the all-contributors specification. Contributions of any kind welcome!

If you should be on this list but you aren't, or you are on the list but don't want to be, please don't hesitate to contact me!

How to Contribute

How to Update Docs

The latest version of the docs is hosted on Github Pages, if you want to help document Simple Transformers below are the steps to edit the docs. Docs are built using Jekyll library, refer to their webpage for a detailed explanation of how it works.

1. Install Jekyll: Run the command gem install bundler jekyll
2. Visualizing the docs on your local computer: In your terminal cd into the docs directory of this repo, eg: cd simpletransformers/docs From the docs directory run this command to serve the Jekyll docs locally: bundle exec jekyll serve Browse to http://localhost:4000 or whatever url you see in the console to visualize the docs.
3. Edit and visualize changes: All the section pages of our docs can be found under docs/_docs directory, you can edit any file you want by following the markdown format and visualize the changes after refreshing the browser tab.

Acknowledgements

None of this would have been possible without the hard work by the HuggingFace team in developing the Transformers library.

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