Introduction | Quick Links | Installation | Get Started | Tutorials | Notes
MindNLP is an open source NLP library based on MindSpore. It supports a platform for solving natural language processing tasks, containing many common approaches in NLP. It can help researchers and developers to construct and train models more conveniently and rapidly.
The master branch works with MindSpore master.
- Comprehensive data processing: Several classical NLP datasets are packaged into friendly module for easy use, such as Multi30k, SQuAD, CoNLL, etc.
- Friendly NLP model toolset: MindNLP provides various configurable components. It is friendly to customize models using MindNLP.
- Easy-to-use engine: MindNLP simplified complicated training process in MindSpore. It supports Trainer and Evaluator interfaces to train and evaluate models easily.
- mindspore >= 1.8.1
To install MindNLP from source, please run:
pip install git+https://github.com/mindspore-ecosystem/mindnlp.gitWe will next quickly implement a sentiment classification task by using mindnlp.
import math
from mindspore import nn
from mindspore import ops
from mindspore.common.initializer import Uniform, HeUniform
from mindnlp.abc import Seq2vecModel
class SentimentClassification(Seq2vecModel):
def construct(self, text):
_, (hidden, _), _ = self.encoder(text)
context = ops.concat((hidden[-2, :, :], hidden[-1, :, :]), axis=1)
output = self.head(context)
return outputThe following are some of the required hyperparameters in the model training process.
# define Models & Loss & Optimizer
hidden_size = 256
output_size = 1
num_layers = 2
bidirectional = True
drop = 0.5
lr = 0.001The dataset was downloaded and preprocessed by calling the interface of dataset in mindnlp.
Load dataset:
from mindnlp.dataset import load
imdb_train, imdb_test = load('imdb', shuffle=True)Initializes the vocab and tokenizer for preprocessing:
from mindnlp.modules import Glove
from mindnlp.transforms import BasicTokenizer
embedding, vocab = Glove.from_pretrained('6B', 100, special_tokens=["<unk>", "<pad>"], dropout=drop)
tokenizer = BasicTokenizer(True)The loaded dataset is preprocessed and divided into training and validation:
from mindnlp.dataset import process
imdb_train = process('imdb', imdb_train, tokenizer=tokenizer, vocab=vocab, \
bucket_boundaries=[400, 500], max_len=600, drop_remainder=True)
imdb_test = process('imdb', imdb_test, tokenizer=tokenizer, vocab=vocab, \
bucket_boundaries=[400, 500], max_len=600, drop_remainder=False)from mindnlp.modules import RNNEncoder
# build encoder
lstm_layer = nn.LSTM(100, hidden_size, num_layers=num_layers, batch_first=True,
dropout=dropout, bidirectional=bidirectional)
encoder = RNNEncoder(embedding, lstm_layer)
# build head
head = nn.SequentialCell([
nn.Dropout(1 - dropout),
nn.Sigmoid(),
nn.Dense(hidden_size * 2, output_size,
weight_init=HeUniform(math.sqrt(5)),
bias_init=Uniform(1 / math.sqrt(hidden_size * 2)))
])
# build network
network = SentimentClassification(encoder, head)
loss = nn.BCELoss(reduction='mean')
optimizer = nn.Adam(network.trainable_params(), learning_rate=lr)Now that we have completed all the preparations, we can begin to train the model.
from mindnlp.engine.metrics import Accuracy
from mindnlp.engine.trainer import Trainer
# define metrics
metric = Accuracy()
# define trainer
trainer = Trainer(network=net, train_dataset=imdb_train, eval_dataset=imdb_valid, metrics=metric,
epochs=5, loss_fn=loss, optimizer=optimizer)
trainer.run(tgt_columns="label")
print("end train")This project is released under the Apache 2.0 license.
The dynamic version is still under development, if you find any issue or have an idea on new features, please don't hesitate to contact us via Github Issues.
MindSpore is an open source project that welcome any contribution and feedback.
We wish that the toolbox and benchmark could serve the growing research
community by providing a flexible as well as standardized toolkit to reimplement existing methods
and develop their own new semantic segmentation methods.
If you find this project useful in your research, please consider citing:
@misc{mindnlp2022,
title={{MindNLP}: a MindSpore NLP library},
author={MindNLP Contributors},
howpublished = {\url{https://github.com/mindlab-ai/mindnlp}},
year={2022}
}