embeddings
ALBERT: A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS - Google Research, Toyota Technological Institute at Chicago This these embeddings represent the outputs generated by the Albert model.
BERT (Bidirectional Encoder Representations from Transformers) provides dense vector representations for natural language by using a deep, pre-trained neural network with the Transformer architecture
BERT (Bidirectional Encoder Representations from Transformers) provides dense vector representations for natural language by using a deep, pre-trained neural network with the Transformer architecture
See https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/embeddings/BertEmbeddingsTestSpec.scala for further reference on how to use this API. Sources:
Sources :
https://arxiv.org/abs/1810.04805
https://github.com/google-research/bert
Paper abstract
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).
BERT (Bidirectional Encoder Representations from Transformers) provides dense vector representations for natural language by using a deep, pre-trained neural network with the Transformer architecture
BERT (Bidirectional Encoder Representations from Transformers) provides dense vector representations for natural language by using a deep, pre-trained neural network with the Transformer architecture
See https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/embeddings/BertSentenceEmbeddingsTestSpec.scala for further reference on how to use this API. Sources:
Sources :
https://arxiv.org/abs/1810.04805
https://github.com/google-research/bert
Paper abstract
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).
This annotator utilizes WordEmbeddings or BertEmbeddings to generate chunk embeddings from either Chunker, NGramGenerator, or NerConverter outputs.
This annotator utilizes WordEmbeddings or BertEmbeddings to generate chunk embeddings from either Chunker, NGramGenerator, or NerConverter outputs.
TIP:
How to explode and convert these embeddings into Vectors or what’s known as Feature column so it can be used in Spark ML regression or clustering functions:
import org.apache.spark.ml.linalg.{Vector, Vectors} // Let's create a UDF to take array of embeddings and output Vectors val convertToVectorUDF = udf((matrix : Seq[Float]) => { Vectors.dense(matrix.toArray.map(_.toDouble)) }) // Now let's explode the sentence_embeddings column and have a new feature column for Spark ML pipelineDF.select(explode($"chunk_embeddings.embeddings").as("chunk_embeddings_exploded")) .withColumn("features", convertToVectorUDF($"chunk_embeddings_exploded"))
See https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/embeddings/ChunkEmbeddingsTestSpec.scala for further reference on how to use this API.
Embeddings from a language model trained on the 1 Billion Word Benchmark.
Embeddings from a language model trained on the 1 Billion Word Benchmark.
Note that this is a very computationally expensive module compared to word embedding modules that only perform embedding lookups. The use of an accelerator is recommended.
word_emb: the character-based word representations with shape [batch_size, max_length, 512]. == word_emb
lstm_outputs1: the first LSTM hidden state with shape [batch_size, max_length, 1024]. === lstm_outputs1
lstm_outputs2: the second LSTM hidden state with shape [batch_size, max_length, 1024]. === lstm_outputs2
elmo: the weighted sum of the 3 layers, where the weights are trainable. This tensor has shape [batch_size, max_length, 1024] == elmo
See https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/embeddings/ElmoEmbeddingsTestSpec.scala for further reference on how to use this API.
Sources :
https://tfhub.dev/google/elmo/3
https://arxiv.org/abs/1802.05365
Paper abstract :
We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals.
This annotator converts the results from WordEmbeddings, BertEmbeddings, or ElmoEmbeddings into sentence or document embeddings by either summing up or averaging all the word embeddings in a sentence or a document (depending on the inputCols).
This annotator converts the results from WordEmbeddings, BertEmbeddings, or ElmoEmbeddings into sentence or document embeddings by either summing up or averaging all the word embeddings in a sentence or a document (depending on the inputCols).
See https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/embeddings/SentenceEmbeddingsTestSpec.scala for further reference on how to use this API.
The Universal Sentence Encoder encodes text into high dimensional vectors that can be used for text classification, semantic similarity, clustering and other natural language tasks.
The Universal Sentence Encoder encodes text into high dimensional vectors that can be used for text classification, semantic similarity, clustering and other natural language tasks.
See https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/embeddings/UniversalSentenceEncoderTestSpec.scala for further reference on how to use this API.
Sources :
https://arxiv.org/abs/1803.11175
https://tfhub.dev/google/universal-sentence-encoder/2
Paper abstract: We present models for encoding sentences into embedding vectors that specifically target transfer learning to other NLP tasks. The models are efficient and result in accurate performance on diverse transfer tasks. Two variants of the encoding models allow for trade-offs between accuracy and compute resources. For both variants, we investigate and report the relationship between model complexity, resource consumption, the availability of transfer task training data, and task performance. Comparisons are made with baselines that use word level transfer learning via pretrained word embeddings as well as baselines do not use any transfer learning. We find that transfer learning using sentence embeddings tends to outperform word level transfer. With transfer learning via sentence embeddings, we observe surprisingly good performance with minimal amounts of supervised training data for a transfer task. We obtain encouraging results on Word Embedding Association Tests (WEAT) targeted at detecting model bias. Our pre-trained sentence encoding models are made freely available for download and on TF Hub.
Word Embeddings lookup annotator that maps tokens to vectors.
Word Embeddings lookup annotator that maps tokens to vectors. See https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/embeddings/WordEmbeddingsTestSpec.scala for further reference on how to use this API.
There are also two convenient functions to retrieve the embeddings coverage with respect to the transformed dataset:
withCoverageColumn(dataset, embeddingsCol, outputCol): Adds a custom column with word coverage stats for the embedded field: (coveredWords, totalWords, coveragePercentage). This creates a new column with statistics for each row.
overallCoverage(dataset, embeddingsCol): Calculates overall word coverage for the whole data in the embedded field. This returns a single coverage object considering all rows in the field.
Word Embeddings lookup annotator that maps tokens to vectors
Word Embeddings lookup annotator that maps tokens to vectors
See https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/ner/NerConverterTest.scala for example usage of this API.
XlnetEmbeddings (XLNet): Generalized Autoregressive Pretraining for Language Understanding
XlnetEmbeddings (XLNet): Generalized Autoregressive Pretraining for Language Understanding
Note that this is a very computationally expensive module compared to word embedding modules that only perform embedding lookups. The use of an accelerator is recommended.
XLNet is a new unsupervised language representation learning method based on a novel generalized permutation language modeling objective. Additionally, XLNet employs Transformer-XL as the backbone model, exhibiting excellent performance for language tasks involving long context. Overall, XLNet achieves state-of-the-art (SOTA) results on various downstream language tasks including question answering, natural language inference, sentiment analysis, and document ranking.
XLNet-Large = https://storage.googleapis.com/xlnet/released_models/cased_L-24_H-1024_A-16.zip | 24-layer, 1024-hidden, 16-heads XLNet-Base = https://storage.googleapis.com/xlnet/released_models/cased_L-12_H-768_A-12.zip | 12-layer, 768-hidden, 12-heads. This model is trained on full data (different from the one in the paper).
ALBERT: A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS - Google Research, Toyota Technological Institute at Chicago This these embeddings represent the outputs generated by the Albert model. All offical Albert releases by google in TF-HUB are supported with this Albert Wrapper:
TF-hub Models :
albert_base = https://tfhub.dev/google/albert_base/3 | 768-embed-dim, 12-layer, 12-heads, 12M parameters
albert_large = https://tfhub.dev/google/albert_large/3 | 1024-embed-dim, 24-layer, 16-heads, 18M parameters
albert_xlarge = https://tfhub.dev/google/albert_xlarge/3 | 2048-embed-dim, 24-layer, 32-heads, 60M parameters
albert_xxlarge = https://tfhub.dev/google/albert_xxlarge/3 | 4096-embed-dim, 12-layer, 64-heads, 235M parameters
This model requires input tokenization with SentencePiece model, which is provided by Spark-NLP (See tokenizers package)
Sources :
https://arxiv.org/pdf/1909.11942.pdf
https://github.com/google-research/ALBERT
https://tfhub.dev/s?q=albert
Paper abstract :
Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks. However, at some point further model increases become harder due to GPU/TPU memory limitations and longer training times. To address these problems, we present two parameterreduction techniques to lower memory consumption and increase the training speed of BERT (Devlin et al., 2019). Comprehensive empirical evidence shows that our proposed methods lead to models that scale much better compared to the original BERT. We also use a self-supervised loss that focuses on modeling inter-sentence coherence, and show it consistently helps downstream tasks with multi-sentence inputs. As a result, our best model establishes new state-of-the-art results on the GLUE, RACE, and SQuAD benchmarks while having fewer parameters compared to BERT-large.
Tips : ALBERT uses repeating layers which results in a small memory footprint, however the computational cost remains similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same number of (repeating) layers.