[P] Fitting (almost) any PyTorch module with just one line, including easy BERT fine-tuning
Hi everyone,
My name is Dima and I wanted to tell you about an open-source library we work on called TamnunML.
Our goal is to provide an easy to use library (with sklearn
interface) for complex model training and fine-tuning. For example, with tamnun
you can train any pytorch
module like this:
“`python 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) “`
or, you can fine tune BERT on your task as easy as: “`python 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) “`
At the moment tamnun
supports training (almost) any pytorch
module using just a “fit” method, easy BERT fine-tuning and model distillation.
You can read more about how to train (almost) any pytroch module with tamnun here
The library github page.
The introduction to TamnunML of the library we published in our blog.
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