AllenNLP是一个开源的自然语言处理（NLP）平台，它的目标是为研究人员和工程师提供一种易于使用的NLP工具箱，以加速自然语言处理研究的发展。它提供了许多已经实现的NLP模型和组件，包括文本分类、命名实体识别、文本分析和机器翻译等。
下面分别介绍如何使用AllenNLP进行文本分类和命名实体识别两个示例。
使用AllenNLP进行文本分类
下面是一个使用AllenNLP进行文本分类的示例，其中我们将使用IMDB电影评论数据集进行情感分析。首先，我们需要在AllenNLP中定义一个文本分类器。以下是一个简单的代码示例：
from typing import Iterator, List, Dict
import torch
import torch.optim as optim
import numpy as np
from allennlp.data import Instance
from allennlp.data.fields import TextField, LabelField
from allennlp.data.dataset_readers import DatasetReader
from allennlp.data.tokenizers import Token
from allennlp.data.token_indexers import TokenIndexer, SingleIdTokenIndexer
from allennlp.models import Model
from allennlp.modules import TextFieldEmbedder, Seq2VecEncoder, FeedForward
from allennlp.modules.seq2vec_encoders import BagOfEmbeddingsEncoder
from allennlp.modules.text_field_embedders import BasicTextFieldEmbedder
from allennlp.training.metrics import CategoricalAccuracy
from allennlp.nn.util import get_text_field_mask
class SimpleClassifier(Model):
def __init__(self,
text_field_embedder: TextFieldEmbedder,
encoder: Seq2VecEncoder,
feedforward: FeedForward,
dropout: float = None) -> None:
super().__init__(vocab)
self.text_field_embedder = text_field_embedder
self.encoder = encoder
self.feedforward = feedforward
self.dropout = torch.nn.Dropout(dropout) if dropout else None
self.accuracy = CategoricalAccuracy()
self.loss_function = torch.nn.CrossEntropyLoss()
def forward(self,
tokens: Dict[str, torch.Tensor],
label: torch.Tensor = None) -> Dict[str, torch.Tensor]:
mask = get_text_field_mask(tokens)
embeddings = self.text_field_embedder(tokens)
encoder_out = self.encoder(embeddings, mask)
if self.dropout:
encoder_out = self.dropout(encoder_out)
logits = self.feedforward(encoder_out)
output = {"logits": logits}
if label is not None:
self.accuracy(logits, label)
output["loss"] = self.loss_function(logits, label)
return output
定义好分类器后，我们需要定义数据读取器，下面是一个简单的示例：
class ImdbDatasetReader(DatasetReader):
def __init__(self, token_indexers: Dict[str, TokenIndexer] = None) -> None:
super().__init__(lazy=False)
self.token_indexers = token_indexers or {"tokens": SingleIdTokenIndexer()}
def text_to_instance(self, text: str, label: str = None) -> Instance:
tokens = [Token(word) for word in text.split()]
text_field = TextField(tokens, self.token_indexers)
fields = {"tokens": text_field}
if label is not None:
fields["label"] = LabelField(label)
return Instance(fields)
def _read(self, file_path: str) -> Iterator[Instance]:
with open(file_path, "r") as text_file:
for line in text_file:
label, text = line.strip().split("\t")
yield self.text_to_instance(text, label)
有了数据读取器和分类器，我们就可以开始训练模型了。下面是一个训练模型的示例：
reader = ImdbDatasetReader()
train_dataset = reader.read("train.txt")
dev_dataset = reader.read("dev.txt")
vocab = Vocabulary.from_instances(train_dataset + dev_dataset)
token_embedding = Embedding(num_embeddings=vocab.get_vocab_size('tokens'),
embedding_dim=EMBEDDING_DIM)
word_embeddings = BasicTextFieldEmbedder({"tokens": token_embedding})
encoder = BagOfEmbeddingsEncoder(EMBEDDING_DIM)
feedforward = torch.nn.Sequential(
torch.nn.Linear(encoder.get_output_dim(), HIDDEN_DIM),
torch.nn.ReLU(),
torch.nn.Linear(HIDDEN_DIM, num_labels))
model = SimpleClassifier(word_embeddings, encoder, feedforward, DROPOUT)
optimizer = optim.Adam(model.parameters())
iterator = BucketIterator(batch_size=BATCH_SIZE, sorting_keys=[("tokens", "num_tokens")])
iterator.index_with(vocab)
trainer = Trainer(model=model,
optimizer=optimizer,
iterator=iterator,
train_dataset=train_dataset,
validation_dataset=dev_dataset,
patience=10,
num_epochs=NUM_EPOCHS,
cuda_device=0)
trainer.train()
训练完成后，我们可以使用训练好的模型对新的电影评论进行情感分析。以下是一个简单的代码示例：
from allennlp.predictors.predictor import Predictor
predictor = Predictor.from_path("path/to/trained/model/file")
result = predictor.predict("This movie is terrible")
以上就是使用AllenNLP进行文本分类的示例。下面我们将介绍如何使用AllenNLP进行命名实体识别。
使用AllenNLP进行命名实体识别
下面是一个使用AllenNLP进行命名实体识别的示例，其中我们将使用CoNLL-2003数据集进行命名实体识别。首先，我们需要在AllenNLP中定义一个命名实体识别器。以下是一个简单的代码示例：
from typing import Iterator, List, Dict
import torch
from torch.nn import Dropout, Linear
from allennlp.data import Instance
from allennlp.data.fields import TextField, SequenceLabelField
from allennlp.data.dataset_readers import DatasetReader
from allennlp.data.tokenizers import Token
from allennlp.data.token_indexers import TokenIndexer, SingleIdTokenIndexer
from allennlp.models import Model
from allennlp.modules import TextFieldEmbedder, Seq2SeqEncoder, TimeDistributed, ConditionalRandomField
from allennlp.modules.seq2vec_encoders import BagOfEmbeddingsEncoder
from allennlp.modules.text_field_embedders import BasicTextFieldEmbedder
from allennlp.training.metrics import CategoricalAccuracy
from allennlp.nn.util import get_text_field_mask
class SimpleTagger(Model):
def __init__(self,
text_field_embedder: TextFieldEmbedder,
encoder: Seq2SeqEncoder,
dropout: float = None,
num_labels: int = None) -> None:
super().__init__(vocab)
self.text_field_embedder = text_field_embedder
self.encoder = encoder
self.time_distributed = TimeDistributed(Linear(encoder.get_output_dim(), num_labels))
self.dropout = Dropout(dropout) if dropout else None
self.loss = torch.nn.CrossEntropyLoss()
self.accuracy = CategoricalAccuracy()
def forward(self,
tokens: Dict[str, torch.Tensor],
tags: torch.Tensor = None) -> Dict[str, torch.Tensor]:
mask = get_text_field_mask(tokens)
embeddings = self.text_field_embedder(tokens)
encoder_out = self.encoder(embeddings, mask)
if self.dropout:
encoder_out = self.dropout(encoder_out)
tag_logits = self.time_distributed(encoder_out)
output = {"tag_logits": tag_logits}
if tags is not None:
self.accuracy(tag_logits, tags, mask.float())
output["loss"] = self.loss(tag_logits, tags, mask.float())
return output
定义好命名实体识别器后，我们需要定义数据读取器，下面是一个简单的示例：
class Conll2003DatasetReader(DatasetReader):
def __init__(self, token_indexers: Dict[str, TokenIndexer] = None) -> None:
super().__init__(lazy=False)
self.token_indexers = token_indexers or {"tokens": SingleIdTokenIndexer()}
def text_to_instance(self, tokens: List[Token], tags: List[str] = None) -> Instance:
token_field = TextField(tokens, self.token_indexers)
fields = {"tokens": token_field}
if tags is not None:
label_field = SequenceLabelField(labels=tags, sequence_field=token_field)
fields["tags"] = label_field
return Instance(fields)
def _read(self, file_path: str) -> Iterator[Instance]:
with open(file_path, encoding="utf-8") as file:
tokens = []
tags = []
for line in file:
line = line.strip().split()
if not line:
if tokens:
yield self.text_to_instance(tokens, tags)
tokens = []
tags = []
else:
token, tag = line
tokens.append(Token(token))
tags.append(tag)
if tokens:
yield self.text_to_instance(tokens, tags)
有了数据读取器和命名实体识别器，我们就可以开始训练模型了。下面是一个训练模型的示例：
reader = Conll2003DatasetReader()
train_dataset = reader.read("train.txt")
dev_dataset = reader.read("dev.txt")
vocab = Vocabulary.from_instances(train_dataset + dev_dataset)
token_embedding = Embedding(num_embeddings=vocab.get_vocab_size('tokens'),
embedding_dim=EMBEDDING_DIM)
word_embeddings = BasicTextFieldEmbedder({"tokens": token_embedding})
encoder = BagOfEmbeddingsEncoder(EMBEDDING_DIM)
model = SimpleTagger(word_embeddings, encoder, DROPOUT, VOCAB.get_vocab_size('labels'))
iterator = BucketIterator(batch_size=BATCH_SIZE, sorting_keys=[("tokens", "num_tokens")])
iterator.index_with(vocab)
trainer = Trainer(model=model,
optimizer=optimizer,
iterator=iterator,
train_dataset=train_dataset,
validation_dataset=dev_dataset,
patience=10,
num_epochs=NUM_EPOCHS,
cuda_device=0)
trainer.train()
训练完成后，我们可以使用训练好的模型对新的文本进行命名实体识别。以下是一个简单的代码示例：
from allennlp.predictors.predictor import Predictor
predictor = Predictor.from_path("path/to/trained/model/file")
result = predictor.predict("John Smith lives in London")
以上就是使用AllenNLP进行命名实体识别的示例。