Getting Started

BLURR is a library designed for fastai developers who want to train and deploy Hugging Face transformers

Named after the fastest transformer (well, at least of the Autobots), BLURR provides both a comprehensive and extensible framework for training and deploying 🤗 huggingface transformer models with fastai >= 2.0.

Utilizing features like fastai’s new @typedispatch and @patch decorators, along with a simple class hiearchy, BLURR provides fastai developers with the ability to train and deploy transformers on a variety of tasks. It includes a high, mid, and low-level API that will allow developers to use much of it out-of-the-box or customize it as needed.

Supported Text/NLP Tasks: - Sequence Classification
- Token Classification
- Question Answering
- Summarization
- Tranlsation
- Language Modeling (Causal and Masked)

Supported Vision Tasks: - In progress

Supported Audio Tasks: - In progress

Install

You can now pip install blurr via pip install ohmeow-blurr

Or, even better as this library is under very active development, create an editable install like this:

git clone https://github.com/ohmeow/blurr.git
cd blurr
pip install -e ".[dev]"

How to use

Please check the documentation for more thorough examples of how to use this package.

The following two packages need to be installed for blurr to work:
1. fastai
2. Hugging Face transformers

Imports

import os, warnings

import torch
from transformers import *
from transformers.utils import logging as hf_logging
from fastai.text.all import *

from blurr.text.data.all import *
from blurr.text.modeling.all import *
warnings.simplefilter("ignore")
hf_logging.set_verbosity_error()

os.environ["TOKENIZERS_PARALLELISM"] = "false"

Get your data

path = untar_data(URLs.IMDB_SAMPLE)

model_path = Path("models")
imdb_df = pd.read_csv(path / "texts.csv")

Get n_labels from data for config later

n_labels = len(imdb_df["label"].unique())

Get your 🤗 objects

model_cls = AutoModelForSequenceClassification

pretrained_model_name = "bert-base-uncased"

config = AutoConfig.from_pretrained(pretrained_model_name)
config.num_labels = n_labels

hf_arch, hf_config, hf_tokenizer, hf_model = get_hf_objects(
    pretrained_model_name,
    model_cls=model_cls, 
    config=config
)

Build your Data 🧱 and your DataLoaders

# single input
blocks = (
    TextBlock(hf_arch, hf_config, hf_tokenizer, hf_model), 
    CategoryBlock
)
dblock = DataBlock(
    blocks=blocks, 
    get_x=ColReader("text"), 
    get_y=ColReader("label"), 
    splitter=ColSplitter()
)

dls = dblock.dataloaders(imdb_df, bs=4)
dls.show_batch(dataloaders=dls, max_n=2, trunc_at=250)
text target
0 raising victor vargas : a review < br / > < br / > you know, raising victor vargas is like sticking your hands into a big, steaming bowl of oatmeal. it's warm and gooey, but you're not sure if it feels right. try as i might, no matter how warm and go negative
1 the shop around the corner is one of the sweetest and most feel - good romantic comedies ever made. there's just no getting around that, and it's hard to actually put one's feeling for this film into words. it's not one of those films that tries too positive

… and 🚂

model = BaseModelWrapper(hf_model)

learn = Learner(
    dls,
    hf_model,
    opt_func=partial(Adam, decouple_wd=True),
    loss_func=CrossEntropyLossFlat(),
    metrics=[accuracy],
    cbs=[BaseModelCallback],
    splitter=blurr_splitter,
)

learn.freeze()

learn.fit_one_cycle(3, lr_max=1e-3)
epoch train_loss valid_loss accuracy time
0 0.628744 0.453862 0.780000 00:21
1 0.367063 0.294906 0.895000 00:22
2 0.238181 0.279067 0.900000 00:22 
learn.show_results(learner=learn, max_n=2, trunc_at=250)
text target prediction
0 the trouble with the book, " memoirs of a geisha " is that it had japanese surfaces but underneath the surfaces it was all an american man's way of thinking. reading the book is like watching a magnificent ballet with great music, sets, and costumes negative negative
1 < br / > < br / > i'm sure things didn't exactly go the same way in the real life of homer hickam as they did in the film adaptation of his book, rocket boys, but the movie " october sky " ( an anagram of the book's title ) is good enough to stand al positive positive

Using the high-level Blurr API

Using the high-level API we can reduce DataBlock, DataLoaders, and Learner creation into a single line of code.

Included in the high-level API is a general BLearner class (pronouned “Blurrner”) that you can use with hand crafted DataLoaders, as well as, task specific BLearners like BLearnerForSequenceClassification that will handle everything given your raw data sourced from a pandas DataFrame, CSV file, or list of dictionaries (for example a huggingface datasets dataset)

learn = BlearnerForSequenceClassification.from_data(
    imdb_df, 
    pretrained_model_name, 
    dl_kwargs={"bs": 4}
)
learn.fit_one_cycle(1, lr_max=1e-3)
epoch train_loss valid_loss f1_score accuracy time
0 0.530218 0.484683 0.789189 0.805000 00:22 
learn.show_results(learner=learn, max_n=2, trunc_at=250)
text target prediction
0 the trouble with the book, " memoirs of a geisha " is that it had japanese surfaces but underneath the surfaces it was all an american man's way of thinking. reading the book is like watching a magnificent ballet with great music, sets, and costumes negative negative
1 < br / > < br / > i'm sure things didn't exactly go the same way in the real life of homer hickam as they did in the film adaptation of his book, rocket boys, but the movie " october sky " ( an anagram of the book's title ) is good enough to stand al positive positive

⭐ Props

A word of gratitude to the following individuals, repos, and articles upon which much of this work is inspired from: