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DATASET
AllenNLP is a free, open-source natural language processing platform for building state of the art models. A GUIDE TO NATURAL LANGUAGE PROCESSING WITH ALLENNLP About this guide. We walk through the basics of using AllenNLP, describing all of the main abstractions used and why we chose them, how to use specific functionality like configuration files or pre-trained representations, and how to build various kinds of models, from simple to complex. ALLENNLP - MOCHA DATASET AllenNLP is a free, open-source natural language processing platform for building state of the art models. ALLENNLP.DATA.DATASET allennlp.data.dataset¶. A Batch represents a collection of Instance s to be fed through a model.. class allennlp.data.dataset.Batch (instances: Iterable) ¶. Bases: collections.abc.Iterable, typing.Generic A batch of Instances. In addition to containing the instances themselves, it contains helper functions for converting the data into tensors. ALLENNLP.DATA.VOCABULARY allennlp.data.vocabulary¶. A Vocabulary maps strings to integers, allowing for strings to be mapped to an out-of-vocabulary token. class allennlp.data.vocabularyINITIALIZERS
An initaliser which preserves output variance for approximately gaussian distributed inputs. This boils down to initialising layers using a uniform distribution in the range (-sqrt(3/dim) * scale, sqrt(3 / dim) * scale), where dim is equal to the input dimension of the parameter and the scale is a constant scaling factor which depends on the non-linearity used. ONTONOTES - ALLENNLP MODELS V2.5.1 This DatasetReader is designed to read in the English OntoNotes v5.0 data in the format used by the CoNLL 2011/2012 shared tasks. In order to use this Reader, you must follow the instructions provided here (v12 release):, which will allow you to download the CoNLL style annotations for the OntoNotes v5.0 release -- LDC2013T19.tgz obtainedfrom LDC.
MEAN_ABSOLUTE_ERROR
AllenNLP is a .. Parameters¶. predictions: torch.Tensor A tensor of predictions of shape (batch_size, ). gold_labels: torch.Tensor A tensor of the same shape as predictions.; mask: torch.BoolTensor, optional (default = None) A tensor of the same shape as predictions.;get_metric¶
TOKENIZER - ALLENNLP V2.5.1 AllenNLP is a .. A Tokenizer splits strings of text into tokens. Typically, this either splits text into word tokens or character tokens, and those are the two tokenizer subclasses we have implemented here, though you could imagine wanting to do other kinds of tokenization for structured or other inputs. CRF_TAGGER - ALLENNLP MODELS V1.4.1 AllenNLP is a .. AllenNLP Models v1.4.1 crf_tagger ALLENNLPGUIDEELMOPUBLICATIONSDOCUMENTATIONALLENNLP INTERPRETDROPDATASET
AllenNLP is a free, open-source natural language processing platform for building state of the art models. A GUIDE TO NATURAL LANGUAGE PROCESSING WITH ALLENNLP About this guide. We walk through the basics of using AllenNLP, describing all of the main abstractions used and why we chose them, how to use specific functionality like configuration files or pre-trained representations, and how to build various kinds of models, from simple to complex. ALLENNLP - MOCHA DATASET AllenNLP is a free, open-source natural language processing platform for building state of the art models. ALLENNLP.DATA.DATASET allennlp.data.dataset¶. A Batch represents a collection of Instance s to be fed through a model.. class allennlp.data.dataset.Batch (instances: Iterable) ¶. Bases: collections.abc.Iterable, typing.Generic A batch of Instances. In addition to containing the instances themselves, it contains helper functions for converting the data into tensors. ALLENNLP.DATA.VOCABULARY allennlp.data.vocabulary¶. A Vocabulary maps strings to integers, allowing for strings to be mapped to an out-of-vocabulary token. class allennlp.data.vocabularyINITIALIZERS
An initaliser which preserves output variance for approximately gaussian distributed inputs. This boils down to initialising layers using a uniform distribution in the range (-sqrt(3/dim) * scale, sqrt(3 / dim) * scale), where dim is equal to the input dimension of the parameter and the scale is a constant scaling factor which depends on the non-linearity used. ONTONOTES - ALLENNLP MODELS V2.5.1 This DatasetReader is designed to read in the English OntoNotes v5.0 data in the format used by the CoNLL 2011/2012 shared tasks. In order to use this Reader, you must follow the instructions provided here (v12 release):, which will allow you to download the CoNLL style annotations for the OntoNotes v5.0 release -- LDC2013T19.tgz obtainedfrom LDC.
MEAN_ABSOLUTE_ERROR
AllenNLP is a .. Parameters¶. predictions: torch.Tensor A tensor of predictions of shape (batch_size, ). gold_labels: torch.Tensor A tensor of the same shape as predictions.; mask: torch.BoolTensor, optional (default = None) A tensor of the same shape as predictions.;get_metric¶
TOKENIZER - ALLENNLP V2.5.1 AllenNLP is a .. A Tokenizer splits strings of text into tokens. Typically, this either splits text into word tokens or character tokens, and those are the two tokenizer subclasses we have implemented here, though you could imagine wanting to do other kinds of tokenization for structured or other inputs. CRF_TAGGER - ALLENNLP MODELS V1.4.1 AllenNLP is a .. AllenNLP Models v1.4.1 crf_tagger A GUIDE TO NATURAL LANGUAGE PROCESSING WITH ALLENNLP About this guide. We walk through the basics of using AllenNLP, describing all of the main abstractions used and why we chose them, how to use specific functionality like configuration files or pre-trained representations, and how to build various kinds of models, from simple to complex. ALLENNLP - QUOREF DATASET AllenNLP is a free, open-source natural language processing platform for building state of the art models.ALLENNLP V2.4.0
AllenNLP is a .. AllenNLP will automatically find any official AI2-maintained plugins that you have installed, but for AllenNLP to find personal or third-party plugins you've installed, you also have to create either a local plugins file named .allennlp_plugins in the directory where you run the allennlp command, or a global plugins file at ~/.allennlp/plugins. ALLENNLP.DATA.FIELDS The text component of this dictionary is suitable to be passed into a TextFieldEmbedder (which handles the additional num_entities dimension without any issues). The linking component of the dictionary can be used however you want to decide which tokens in the utterance correspond to which entities in the knowledge graph.. In order to create the text component, we use the same dictionary of ONTONOTES - ALLENNLP MODELS V2.5.1 This DatasetReader is designed to read in the English OntoNotes v5.0 data in the format used by the CoNLL 2011/2012 shared tasks. In order to use this Reader, you must follow the instructions provided here (v12 release):, which will allow you to download the CoNLL style annotations for the OntoNotes v5.0 release -- LDC2013T19.tgz obtainedfrom LDC.
ALLENNLP.DATA.ITERATORS allennlp.data.iterators¶. The various DataIterator subclasses can be used to iterate over datasets with different batching and padding schemes.. DataIterator. BasicIterator. BucketIterator. MultiprocessIterator. HomogeneousBatchIterator. SameLanguageIterator.PassThroughIterator
UNIVERSAL_DEPENDENCIES Reads a file in the conllu Universal Dependencies format. Parameters¶. token_indexers: Dict, optional (default = {"tokens": SingleIdTokenIndexer()}) The token indexers to be applied to the words TextField. use_language_specific_pos: bool, optional (default = False) Whether to use UD POS tags, or to use the language specific POS tags provided in the conllu format.BIDIRECTIONAL_LM
The BidirectionalLanguageModel applies a bidirectional "contextualizing" Seq2SeqEncoder to uncontextualized embeddings, using a SoftmaxLoss module (defined above) to compute the language modeling loss.. It is IMPORTANT that your bidirectional Seq2SeqEncoder does not do any "peeking ahead". That is, for its forward direction it should only consider embeddings at previous timesteps, and MULTIPROCESS_DATA_LOADER Note. In a typical AllenNLP configuration file, the reader and data_path parameters don't get an entry under the data_loader.The reader is constructed separately from the corresponding dataset_reader params, and the data_path is taken from the train_data_path, validation_data_path, or test_data_path. PRETRAINED - ALLENNLP MODELS V2.5.1 AllenNLP is a .. Returns the Predictor corresponding to the given model_id.. The model_id should be key present in the mapping returned by get_pretrained_models. ALLENNLPGUIDEELMOPUBLICATIONSDOCUMENTATIONALLENNLP INTERPRETDROPDATASET
AllenNLP is a free, open-source natural language processing platform for building state of the art models. HOME — ALLENNLP 0.9.0 DOCUMENTATION Home¶. Built on PyTorch, AllenNLP makes it easy to design and evaluate new deep learning models for nearly any NLP problem, along with the infrastructure to easily run them in the cloud or on yourlaptop.
ALLENNLP.DATA.VOCABULARY allennlp.data.vocabulary¶. A Vocabulary maps strings to integers, allowing for strings to be mapped to an out-of-vocabulary token. class allennlp.data.vocabulary ALLENNLP.DATA.FIELDS The text component of this dictionary is suitable to be passed into a TextFieldEmbedder (which handles the additional num_entities dimension without any issues). The linking component of the dictionary can be used however you want to decide which tokens in the utterance correspond to which entities in the knowledge graph.. In order to create the text component, we use the same dictionary of ALLENNLP.DATA.DATASET allennlp.data.dataset¶. A Batch represents a collection of Instance s to be fed through a model.. class allennlp.data.dataset.Batch (instances: Iterable) ¶. Bases: collections.abc.Iterable, typing.Generic A batch of Instances. In addition to containing the instances themselves, it contains helper functions for converting the data into tensors. ALLENNLP.DATA.ITERATORS allennlp.data.iterators¶. The various DataIterator subclasses can be used to iterate over datasets with different batching and padding schemes.. DataIterator. BasicIterator. BucketIterator. MultiprocessIterator. HomogeneousBatchIterator. SameLanguageIterator.PassThroughIterator
UNIVERSAL_DEPENDENCIES Reads a file in the conllu Universal Dependencies format. Parameters¶. token_indexers: Dict, optional (default = {"tokens": SingleIdTokenIndexer()}) The token indexers to be applied to the words TextField. use_language_specific_pos: bool, optional (default = False) Whether to use UD POS tags, or to use the language specific POS tags provided in the conllu format.INITIALIZERS
An initaliser which preserves output variance for approximately gaussian distributed inputs. This boils down to initialising layers using a uniform distribution in the range (-sqrt(3/dim) * scale, sqrt(3 / dim) * scale), where dim is equal to the input dimension of the parameter and the scale is a constant scaling factor which depends on the non-linearity used.FEEDFORWARD
This Module is a feed-forward neural network, just a sequence of Linear layers with activation functions in between.. Parameters¶. input_dim: int The dimensionality of the input. We assume the input has shape (batch_size, input_dim).; num_layers: int The number of Linear layers to apply to the input.; hidden_dims: Union The output dimension of each of the Linear layers. BILINEAR_MATRIX_ATTENTION Computes attention between two matrices using a bilinear attention function. This function has a matrix of weights W and a bias b, and the similarity between the two matrices X and Y is computed as X W Y^T + b.. Registered as a MatrixAttention with name "bilinear"..Parameters¶
ALLENNLPGUIDEELMOPUBLICATIONSDOCUMENTATIONALLENNLP INTERPRETDROPDATASET
AllenNLP is a free, open-source natural language processing platform for building state of the art models. HOME — ALLENNLP 0.9.0 DOCUMENTATION Home¶. Built on PyTorch, AllenNLP makes it easy to design and evaluate new deep learning models for nearly any NLP problem, along with the infrastructure to easily run them in the cloud or on yourlaptop.
ALLENNLP.DATA.VOCABULARY allennlp.data.vocabulary¶. A Vocabulary maps strings to integers, allowing for strings to be mapped to an out-of-vocabulary token. class allennlp.data.vocabulary ALLENNLP.DATA.FIELDS The text component of this dictionary is suitable to be passed into a TextFieldEmbedder (which handles the additional num_entities dimension without any issues). The linking component of the dictionary can be used however you want to decide which tokens in the utterance correspond to which entities in the knowledge graph.. In order to create the text component, we use the same dictionary of ALLENNLP.DATA.DATASET allennlp.data.dataset¶. A Batch represents a collection of Instance s to be fed through a model.. class allennlp.data.dataset.Batch (instances: Iterable) ¶. Bases: collections.abc.Iterable, typing.Generic A batch of Instances. In addition to containing the instances themselves, it contains helper functions for converting the data into tensors. ALLENNLP.DATA.ITERATORS allennlp.data.iterators¶. The various DataIterator subclasses can be used to iterate over datasets with different batching and padding schemes.. DataIterator. BasicIterator. BucketIterator. MultiprocessIterator. HomogeneousBatchIterator. SameLanguageIterator.PassThroughIterator
UNIVERSAL_DEPENDENCIES Reads a file in the conllu Universal Dependencies format. Parameters¶. token_indexers: Dict, optional (default = {"tokens": SingleIdTokenIndexer()}) The token indexers to be applied to the words TextField. use_language_specific_pos: bool, optional (default = False) Whether to use UD POS tags, or to use the language specific POS tags provided in the conllu format.INITIALIZERS
An initaliser which preserves output variance for approximately gaussian distributed inputs. This boils down to initialising layers using a uniform distribution in the range (-sqrt(3/dim) * scale, sqrt(3 / dim) * scale), where dim is equal to the input dimension of the parameter and the scale is a constant scaling factor which depends on the non-linearity used.FEEDFORWARD
This Module is a feed-forward neural network, just a sequence of Linear layers with activation functions in between.. Parameters¶. input_dim: int The dimensionality of the input. We assume the input has shape (batch_size, input_dim).; num_layers: int The number of Linear layers to apply to the input.; hidden_dims: Union The output dimension of each of the Linear layers. BILINEAR_MATRIX_ATTENTION Computes attention between two matrices using a bilinear attention function. This function has a matrix of weights W and a bias b, and the similarity between the two matrices X and Y is computed as X W Y^T + b.. Registered as a MatrixAttention with name "bilinear"..Parameters¶
ALLENNLP - QUOREF DATASET AllenNLP is a free, open-source natural language processing platform for building state of the art models.ALLENNLP V2.4.0
AllenNLP is a .. AllenNLP will automatically find any official AI2-maintained plugins that you have installed, but for AllenNLP to find personal or third-party plugins you've installed, you also have to create either a local plugins file named .allennlp_plugins in the directory where you run the allennlp command, or a global plugins file at ~/.allennlp/plugins.INITIALIZERS
An initaliser which preserves output variance for approximately gaussian distributed inputs. This boils down to initialising layers using a uniform distribution in the range (-sqrt(3/dim) * scale, sqrt(3 / dim) * scale), where dim is equal to the input dimension of the parameter and the scale is a constant scaling factor which depends on the non-linearity used.FEEDFORWARD
This Module is a feed-forward neural network, just a sequence of Linear layers with activation functions in between.. Parameters¶. input_dim: int The dimensionality of the input. We assume the input has shape (batch_size, input_dim).; num_layers: int The number of Linear layers to apply to the input.; hidden_dims: Union The output dimension of each of the Linear layers. COMMON ARCHITECTURES · A GUIDE TO NATURAL LANGUAGE The main modeling operations done on natural language inputs include summarizing sequences, contextualizing sequences (that is, computing contextualized embeddings from sequences), modeling spans within a longer sequence, and modeling similarities between sequences usingattention.
YOUR FIRST MODEL · A GUIDE TO NATURAL LANGUAGE PROCESSING In this section of the guide, we'll give a quick start on one the most basic things you can do with AllenNLP: text classification. We give a brief introduction to text classification, then implement a simple classifier that decides whether a movie review expresses positive ornegative sentiment.
DATA_LOADER
A DataLoader is responsible for generating batches of instances from a DatasetReader, or another source of data.. This is purely an abstract base class. All concrete subclasses must provide implementations of the following methods: __iter__() that creates an iterable of TensorDicts, iter_instances() that creates an iterable of Instances, index_with() that should index the data with a PRETRAINED - ALLENNLP MODELS V1.5.0 AllenNLP is a .. Returns the Predictor corresponding to the given model_id.. The model_id should be key present in the mapping returned by get_pretrained_models. TOKENIZER - ALLENNLP V2.4.0 AllenNLP is a .. A Tokenizer splits strings of text into tokens. Typically, this either splits text into word tokens or character tokens, and those are the two tokenizer subclasses we have implemented here, though you could imagine wanting to do other kinds of tokenization for structured or other inputs.TOKEN_INDEXER
The Vocabulary needs to assign indices to whatever strings we see in the training data (possibly doing some frequency filtering and using an OOV, or out of vocabulary, token). This method takes a token and a dictionary of counts and increments counts for whatever vocabulary items are present in the token. If this is a single token ID representation, the vocabulary item is likely the token itself.* Home
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ALLENNLP
AN OPEN-SOURCE NLP RESEARCH LIBRARY, BUILT ON PYTORCH View Demo Get Started AllenNLP is a free, open-source project from AI2.
DEEP LEARNING FOR NLP AllenNLP makes it easy to design and evaluate new deep learning models for nearly any NLP problem, along with the infrastructure to easily run them in the cloud or on your laptop.Get Started
STATE OF THE ART MODELS AllenNLP includes reference implementations of high quality models for both core NLP problems (e.g. semantic role labeling) and NLP applications (e.g. textual entailment).View Demo
PROUDLY BUILT BY AI2 AllenNLP is built and maintained by the Allen Institute for AI, in close collaboration with researchers at the University of Washington and elsewhere. With a dedicated team of best-in-field researchers and software engineers, the AllenNLP project is uniquely positioned for long-term growth alongside a vibrant open-source developmentcommunity.
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