simpletransformers 0.1.1

A wrapper for the Transformers library by Hugging Face.

Pytorch-Transformers-Classification

This library is based on the Pytorch-Transformers library by HuggingFace. Using this library, you can quickly train and evaluate Transformer models.

Please refer to this Medium article for further information on how this project works.

Please note that the documentation is still being written.

Table of contents

• Setup
  • With Conda
• Usage
  • Minimal Start
  • Current Pretrained Models
• 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 transformers python pandas tqdm jupyter
conda activate transformers
   If using cuda:
   conda install pytorch cudatoolkit=10.0 -c pytorch
   else:
   conda install pytorch cpuonly -c pytorch
conda install -c anaconda scipy
conda install -c anaconda scikit-learn
pip install transformers
pip install tensorboardx

3. Clone repo. pip install simpletransformers

Usage

Minimal Start

from simpletransformers.model import TransformerModel
import pandas as pd


# Train and Evaluation data needs to be in a Pandas Dataframe of two columns. The first column is the text with type str, and the second column in the label with type int.
train_data = [['Example sentence belonging to class 1', 1], ['Example sentence belonging to class 0', 0]]
train_df = pd.DataFrame(train_data)

eval_data = [['Example eval sentence belonging to class 1', 1], ['Example eval sentence belonging to class 0', 0]]
eval_df = pd.DataFrame(eval_data)

# Create a TransformerModel
model = TransformerModel('roberta', 'roberta-base')

# Train the model
model.train_model(train_df)

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

Current Pretrained Models

The table below shows the currently available model types and their models. You can use any of these by setting the model_type and model_name in the args dictionary. For more information about pretrained models, see HuggingFace docs.

Architecture	Model Type	Model Name	Details
BERT	bert	bert-base-uncased	12-layer, 768-hidden, 12-heads, 110M parameters. Trained on lower-cased English text.
BERT	bert	bert-large-uncased	24-layer, 1024-hidden, 16-heads, 340M parameters. Trained on lower-cased English text.
BERT	bert	bert-base-cased	12-layer, 768-hidden, 12-heads, 110M parameters. Trained on cased English text.
BERT	bert	bert-large-cased	24-layer, 1024-hidden, 16-heads, 340M parameters. Trained on cased English text.
BERT	bert	bert-base-multilingual-uncased	(Original, not recommended) 12-layer, 768-hidden, 12-heads, 110M parameters. Trained on lower-cased text in the top 102 languages with the largest Wikipedias
BERT	bert	bert-base-multilingual-cased	(New, recommended) 12-layer, 768-hidden, 12-heads, 110M parameters. Trained on cased text in the top 104 languages with the largest Wikipedias
BERT	bert	bert-base-chinese	12-layer, 768-hidden, 12-heads, 110M parameters. Trained on cased Chinese Simplified and Traditional text.
BERT	bert	bert-base-german-cased	12-layer, 768-hidden, 12-heads, 110M parameters. Trained on cased German text by Deepset.ai
BERT	bert	bert-large-uncased-whole-word-masking	24-layer, 1024-hidden, 16-heads, 340M parameters. Trained on lower-cased English text using Whole-Word-Masking
BERT	bert	bert-large-cased-whole-word-masking	24-layer, 1024-hidden, 16-heads, 340M parameters. Trained on cased English text using Whole-Word-Masking
BERT	bert	bert-large-uncased-whole-word-masking-finetuned-squad	24-layer, 1024-hidden, 16-heads, 340M parameters. The bert-large-uncased-whole-word-masking model fine-tuned on SQuAD
BERT	bert	bert-large-cased-whole-word-masking-finetuned-squad	24-layer, 1024-hidden, 16-heads, 340M parameters The bert-large-cased-whole-word-masking model fine-tuned on SQuAD
BERT	bert	bert-base-cased-finetuned-mrpc	12-layer, 768-hidden, 12-heads, 110M parameters. The bert-base-cased model fine-tuned on MRPC
XLNet	xlnet	xlnet-base-cased	12-layer, 768-hidden, 12-heads, 110M parameters. XLNet English model
XLNet	xlnet	xlnet-large-cased	24-layer, 1024-hidden, 16-heads, 340M parameters. XLNet Large English model
XLM	xlm	xlm-mlm-en-2048	12-layer, 2048-hidden, 16-heads XLM English model
XLM	xlm	xlm-mlm-ende-1024	6-layer, 1024-hidden, 8-heads XLM English-German Multi-language model
XLM	xlm	xlm-mlm-enfr-1024	6-layer, 1024-hidden, 8-heads XLM English-French Multi-language model
XLM	xlm	xlm-mlm-enro-1024	6-layer, 1024-hidden, 8-heads XLM English-Romanian Multi-language model
XLM	xlm	xlm-mlm-xnli15-1024	12-layer, 1024-hidden, 8-heads XLM Model pre-trained with MLM on the 15 XNLI languages
XLM	xlm	xlm-mlm-tlm-xnli15-1024	12-layer, 1024-hidden, 8-heads XLM Model pre-trained with MLM + TLM on the 15 XNLI languages
XLM	xlm	xlm-clm-enfr-1024	12-layer, 1024-hidden, 8-heads XLM English model trained with CLM (Causal Language Modeling)
XLM	xlm	xlm-clm-ende-1024	6-layer, 1024-hidden, 8-heads XLM English-German Multi-language model trained with CLM (Causal Language Modeling)
RoBERTa	roberta	roberta-base	125M parameters RoBERTa using the BERT-base architecture
RoBERTa	roberta	roberta-large	24-layer, 1024-hidden, 16-heads, 355M parameters RoBERTa using the BERT-large architecture
RoBERTa	roberta	roberta-large-mnli	24-layer, 1024-hidden, 16-heads, 355M parameters roberta-large fine-tuned on MNLI.

Acknowledgements

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