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
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]"
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:
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"path = untar_data(URLs.IMDB_SAMPLE)
model_path = Path("models")
imdb_df = pd.read_csv(path / "texts.csv")n_labels = len(imdb_df["label"].unique())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
)# 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 |
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 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 |
A word of gratitude to the following individuals, repos, and articles upon which much of this work is inspired from:
- The wonderful community that is the fastai forum and especially the tireless work of both Jeremy and Sylvain in building this amazing framework and place to learn deep learning.
- All the great tokenizers, transformers, docs, examples, and people over at huggingface
- FastHugs
- Fastai with 🤗Transformers (BERT, RoBERTa, XLNet, XLM, DistilBERT)
- Fastai integration with BERT: Multi-label text classification identifying toxicity in texts
- fastinference