Tamnun ML

tamnun is a python framework for Machine and Deep learning algorithms and methods especially in the field of Natural Language Processing and Transfer Learning. The aim of tamnun is to provide an easy to use interfaces to build powerful models based on most recent SOTA methods.

Getting Started

tamnun depends on several other machine learning and deep learning frameworks like pytorch, keras and others. To install tamnun and all it's dependencies run:

$ git clone https://github.com/hiredscorelabs/tamnun-ml
$ cd tamnun-ml
$ python setup.py install

Or using PyPI:

pip install tamnun

Jump in and try out an example:

$ cd examples
$ python finetune_bert.py


BERT stands for Bidirectional Encoder Representations from Transformers which is a language model trained by Google and introduced in their paper.
Here we use the excellent PyTorch-Pretrained-BERT library and wrap it to provide an easy to use scikit-learn interface for easy BERT fine-tuning. At the moment, tamnun BERT classifier supports binary and multi-class classification. To fine-tune BERT on a specific task:

from tamnun.bert import BertClassifier, BertVectorizer
from sklearn.pipeline import make_pipeline

clf = make_pipeline(BertVectorizer(), BertClassifier(num_of_classes=2)).fit(train_X, train_y)
predicted = clf.predict(test_X)

Please see this notebook for full code example.

Fitting (almost) any PyTorch Module using just one line

You can use the TorchEstimator object to fit any pytorch module with just one line:

from torch import nn
from tamnun.core import TorchEstimator

module = nn.Linear(128, 2)
clf = TorchEstimator(module, task_type='classification').fit(train_X, train_y)

See this file for a full example of fitting nn.Linear module on the MNIST (classification of handwritten digits) dataset.

Distiller Transfer Learning

This module distills a very big (like BERT) model into a much smaller model. Inspired by this paper.

from tamnun.bert import BertClassifier, BertVectorizer
from tamnun.transfer import Distiller

bert_clf =  make_pipeline(BertVectorizer(do_truncate=True, max_len=3), BertClassifier(num_of_classes=2))
distilled_clf = make_pipeline(CountVectorizer(ngram_range=(1,3)), LinearRegression())

distiller = Distiller(teacher_model=bert_clf, teacher_predict_func=bert_clf.decision_function, student_model=distilled_clf).fit(train_texts, train_y, unlabeled_X=unlabeled_texts)

predicted_logits = distiller.transform(test_texts)

For full BERT distillation example see this notebook.