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<!DOCTYPE html> <html lang="en-US"> <head> <!--[if IE 8]> <html class="ie8" lang="en"> <![endif]--><!--[if IE 9]> <html class="ie9" lang="en"> <![endif]--><!--[if gt IE 8]><!--><!--<![endif]--> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>How to use bert embeddings</title> <meta name="description" content="How to use bert embeddings"> </head> <body> <div class="td-container"> <!-- breadcrumbs --> <div class="td-crumb-container"> <div class="entry-crumbs" itemscope="" itemtype=""><span class="td-bred-first"><br> </span><span class="td-bred-no-url-last"></span></div> </div> <!-- post content --> <article id="post-26097" class="post-26097 post type-post status-publish format-standard hentry category-zfs tag-zfs" itemscope="" itemtype=""> </article> <div class="td-post-header"> <header class="td-post-title"> </header> <h1 class="entry-title">How to use bert embeddings</h1> <div class="td-module-meta-info"><br> </div> </div> <div class="td-post-content"> <div class="td-post-featured-image"><figure><img class="entry-thumb td-modal-image" src="" srcset=" 636w, 400w" sizes="(max-width: 636px) 100vw, 636px" alt="ZFS Compression Performance Lz4 Gzip 7 Off Time" title="ZFS Compression Performance Lz4 Gzip 7 Off Time" height="374" width="636"><figcaption class="wp-caption-text"></figcaption></figure></div> <p>1, and 3. This is useful when using recurrent layers which may take variable length input. The skipgram model learns to predict a target word thanks to a nearby word. BERT can be trained on sentence pairs for tasks that take sentence pairs as input (e. You can also perform max-pooling or use the embedding from the CLS token. You can get a much more in-depth explanation of word embeddings, its different types, and how to use them on a dataset in the below article. Then you can feed these embeddings to your existing model – a process the paper shows yield results not far behind fine-tuning BERT on a task such as named-entity recognition. 2. In this tutorial, you discovered how to use word embeddings for deep learning in Python with Keras. How to learn a word embedding while fitting a neural network. We can access this functionality with the following code: BERT (Bidirectional Encoder Representations from Transformers) is a recent paper published by researchers at Google AI Language. Well, you’re right – mostly. The goal of this project is to obtain the token embedding from BERT's pre-trained model. The list of pre-trained BERT models available in GluonNLP can be found here. Building upon BERT, a | Find, read and cite all the research you need on ResearchGate We use cookies to make interactions with our website easy and meaningful, to better understand the use of The main difference between the word embeddings of Word2vec, Glove, ELMo and BERT is that * Word2vec and Glove word embeddings are context independent- these models output just one vector (embedding) for each word, combining all the different sens The construction of BERT makes it unsuitable for semantic similarity search as well as for unsupervised tasks like clustering. BERT, introduced by Google in 2018, provides embeddings for words as well as sentences. For most downstream NLP tasks, CWEs drastically improved the perfor-mance of neural architectures compared to static word embeddings. 13 Nov 2019 3. Token Download Open Datasets on 1000s of Projects + Share Projects on One Platform. $\endgroup$ – soloice Feb 19 at 11:05 Aug 13, 2019 · BERT provided a leap in accuracy for NLU tasks that brought high-quality language-based services within the reach of companies across many industries. Nov 26, 2019 · The more common way to use BERT is to fine-tune the model on your dataset, but that requires a GPU and at least a few hours. ' #1. Flexible Data Ingestion. The goal of this project is to obtain the sentence and token embedding from BERT's pre-trained model. It has caused a stir in the Machine Learning community by presenting state-of-the-art results in a wide variety of NLP tasks, including Question Answering (SQuAD v1. ( word_embed): HybridSequential( (0): Embedding(30522 -> 768, float32) (1): 29 Sep 2019 Sys. 2 Contextualized Word Embeddings The idea of modeling sentence or context-level se-mantics together with word-level semantics proved to be a powerful innovation. Oct 17, 2018 · TL;DR. Embedding. BERTEmbedding is based on keras-bert. 15 Jul 2019 You can read more about word embeddings in this post. We found that in multiple cases the performance of ConveRT + classifier without fine-tuning is quantitatively comparable to BERT + classifier with fine-tuning. mask_zero : Whether or not the input value 0 is a special "padding" value that should be masked out. More importantly, rather than simply providing word embeddings as features, BERT can be incorporated into a downstream task and gets fine-tuned as an integrated task-specific architecture. BERT has, in general, been found to be superior to ELMo and far superior to non-contextual embeddings on a variety of tasks, including those in the clinical domain (Si et al. Takeaways. First, we use the BERT model (instantiated from bert-base-uncased) to map tokens in a sentence to the output embeddings from BERT. Here's a sketch:. In this article, I will explain the implementation details of the embedding layers in BERT, namely the Token Embeddings, Segment Embeddings, and the Position Embeddings. Input embeddings for a given batch are generated during the train step, before the forward pass of the BiDAF model. BERT token level embedding with MxNet. set it to NONE for dynamically using the longest sequence in a (mini)batch. Comparing XLnet to BERT, ELMo, and other unidirectional language models. I highly recommend this article – Serving Google BERT in Production using Tensorflow and ZeroMQ . (1 and its blog) BERT appears to encode syntactic and semantic features in word vectors in complementary subspaces (2 and its blog). LongTensor (since the indices are integers, not floats). 2 Available pre-trained BERT models. from_pretrained('bert-base-uncased') sentence='I really enjoyed this movie a lot. In this tutorial I’ll show you how to use BERT with the huggingface PyTorch library to quickly and efficiently fine-tune a model to get near state of the art performance in sentence classification. -max_seq_len: maximum length of a sequence, longer sequence will be trimmed on the right side. My work is similar to it but is different since I have used BERT embeddings rather than word2vec or Doc2vec embeddings and also I have used a neural network to further find the similarity between two document vectors. Many NLP tasks are benefit from BERT to get the SOTA. The next layer in our model is a Pooling model: In that case, we perform mean-pooling. Word Embeddings in Pytorch ¶. One quirk is that BERT uses wordpiece embeddings so we need to use a special tokenizer. In this approach, a pre-trained neural network produces word embeddings which are then used as features in NLP models. Usage . ,2014). Our final model uses fine-tuned BERT embeddings. So if I were to build a LM using ULMFiT and get the embeddings, could I With this you can easily get BERT-encoded text and then use the 6 Apr 2019 Pre-trained language representation models, such as BERT, capture a We demonstrate that using clinical specific contextual embeddings 6 Dec 2018 Word embeddings can capture many different properties of a word and . One thing describes another, even though those two things are radically different. In it, we take an in-depth look at the word embeddings produced by BERT, show you how to create your own in a Google Colab notebook, and tips on how to implement and use these embeddings in your production pipeline. , 2013 was published and the software package word2vec was made public available a new era in NLP started on which word embeddings, also referred to as word vectors, play a crucial role. How to use a pre-trained word embedding in a neural network. What are the possible ways to do that? Here is an awesome project that use BERT to generate sentence embeddings and serve the model in scale: hanxiao/bert-as-service Mapping a variable-length sentence to a fixed-length vector using Mar 18, 2019 · So in simple terms, word embeddings are the text blocks that are converted into numbers for performing NLP tasks. This module is standard in almost every previous reading embeddings_constraint: Constraint function applied to the embeddings matrix (see constraints). As we will show, this common practice yields rather bad sentence embeddings, often worse than averaging GloVe embeddings (Pennington et al. Many thanks to James Wexler for help with this note. The open source release also includes code to run pre-training, although we believe the majority of NLP researchers who use BERT will never need to pre-train their own models from scratch. Good for people and companies building NLP systems. This progress has left the research lab and started powering some of the leading digital products. Maybe you can think of using BERT embeddings to train CNN or RNN classifier, in such case you can try to obtain the embeddings of BERT using a small batch size (it can be as low as 1), and then use these embeddings to further train your CNN or RNN classifier. The embeddings itself are wrapped into our simple embedding interface so that they can be used like any other embedding. To extract the features of all tokens: from keras_bert import extract_embeddings model_path = 'xxx/yyy/uncased_L-12_H-768_A-12' texts = ['all work and no play', 'makes jack a dull boy~'] embeddings = extract BERTEmbedding is based on keras-bert. 5. Just like ELMo, you can use the pre-trained BERT to create contextualized word embeddings. Extracting chemical-protein relations with ensembles of SVM and deep learning models. You’re probably thinking that switching to BERT is mostly the same as above. Furthermore, the amount of task-specific customization is extremely limited, suggesting that the information needed to accomplish all of these tasks is contained in the BERT embedding and in a very explicit form. pip install bert-serving-client You can also use it as a context manager: with BertClient () encoded sentence/token-level embeddings in sending order. For SensEval-3, we achieve a new state-of-the-art result. Essentially, the Transformer stacks a layer that maps sequences to sequences, so the output is also a sequence of vectors with a 1:1 correspondence between input and output tokens at the same index. 1), Natural Language Inference (MNLI), and others. BERT, published by Google, is new way to obtain pre-trained language model word representation. For example, BERT would produce different embeddings for Mercury in the following two sentences: “Mercury is visible in the night sky” and “Mercury is often Nov 01, 2019 · How to Use BERT to Generate Meta Descriptions at Scale. A Visual Guide to Using BERT for the First Time Progress has been rapidly accelerating in machine learning models that process language over the last couple of years. BERT. This series will review the strengths and weaknesses of using pre-trained word embeddings and demonstrate how to incorporate more complex semantic representation schemes such as Semantic Role Labeling, Abstract Meaning Representation and The fine-tuning approach isn’t the only way to use BERT. 3) on 2 datasets. BERTEmbedding support BERT variants like ERNIE, but need to load the tensorflow checkpoint. By Chris McCormick and Nick Ryan. We can use its * Word embeddings as is. In this paper, I have developed a semantics-oriented search engine using neural networks and BERT embeddings that can search for query and rank the documents in the order of the most meaningful to least meaningful. The vectors we use to represent words are called neural word embeddings, and representations are strange. ” BERT is a contextual model, which means that word embeddings are generated based on the context of the word’s use in a sentence, and thus a single word can have multiple embeddings. In this paper, we focus on the classification of books using short descriptive texts (cover blurbs) and additional metadata. 1 Data We trained the varied BERT + BiDAF models introduced above (sections 3. And provide it does - at the time that the BERT paper was published in 2018, BERT-based NLP models have surpassed the previous state-of-the-art results on eleven different NLP tasks In such case, you may find the embeddings of BERT useful than fine-tuning on BERT itself. 3. Context-free models such as word2vec or GloVe generate a single word embedding representation for each word in the vocabulary, where BERT is If using a BERT or ELMo model, the transformation to word embeddings and target task (e. BERT has a few quirks that make it slightly different from your traditional model. You can also combine multiple poolings together. The paper found that the best embeddings are achieved by concatenating the last four layers of the encoder. Instead of reading the text from left to right or from right to left, BERT, using an attention mechanism which is called Transformer encoder 2, reads the entire word sequences at once. Jul 22, 2019 · BERT (Bidirectional Encoder Representations from Transformers), released in late 2018, is the model we will use in this tutorial to provide readers with a better understanding of and practical guidance for using transfer learning models in NLP. In this section, we will learn how to use BERT's embeddings for our NLP Contextual word embedding models such as ELMo and BERT have dramatically We demonstrate that using a domain-specific model yields performance Bidirectional Encoder Representations from Transformers (BERT) is a technique for NLP unsupervised language representation, pre-trained using only a plain text corpus. … Mar 27, 2019 · In the following example, I will use bert-base-uncased pre-trained model. After fine-tuning on a downstream task, the embedding of this [CLS] token or pooled_output as they call it in the hugging face implementation represents the sentence embedding. How to use BERT - Sentence similarity From what I understand, BERT provides contextualized embeddings that are not deterministic the way Word2Vec embeddings (i. These embeddings are then fed into the remaining parts of our modified BiDAF model, described in section 3. 25 Sep 2019 A Look Under the Hood; Using BERT for Text Classification (Python . is to obtain the sentence and token embedding from BERT's pre-trained model. In this publication, we present Sentence-BERT (SBERT), a modification of the pretrained BERT network that use siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be compared using cosine-similarity. bi-directional transformer encoder preceded and followed by single embedding layers. Building upon BERT, a deep neural language model, we demonstrate how to combine text representations with metadata and knowledge graph embeddings, which encode author information. Sep 05, 2019 · BERT seeks to provide a pre-trained method for obtaining contextualized word embeddings, which can then be used for a wide variety of downstream NLP tasks. Learn about Python text classification with Keras. At the same time, empirical study of parse tree embeddings in BERT shows that there may be more to the story, with additional quantitative aspects to parse tree representations. Here’s a list of words associated with “Sweden” using Word2vec, in order of proximity: The nations of Scandinavia and several wealthy, northern European, Germanic countries are among the top nine. Aug 03, 2019 · Please check out the post I co-authored with Chris McCormick on BERT Word Embeddings here. As Elvis Costello said: “Writing about music is like dancing about architecture. Dec 12, 2019 · First, we use the BERT model (instantiated from bert-base-uncased) to map tokens in a sentence to the output embeddings from BERT. Bert Embeddings. 1 BERT Embeddings In our project, we use BERT [1], a current state of the art language representation system to generate word embeddings. In this tutorial, the BERT model we will use is BERT BASE trained on an uncased corpus of books and the English Wikipedia dataset in the GluonNLP model zoo. Since the advent of word2vec, neural word embeddings have become a go to method for encapsulating distributional semantics in text applications. , 2019 ) . Using this new objective, BERT is able to achieve state-of-the-art performance on a variety of tasks in the GLUE benchmark. embeddings. 3. Sep 17, 2019 · Positional embeddings: A positional embedding is added to each token to indicate its position in the sentence. In such case, you may find the embeddings of BERT useful than fine-tuning on BERT itself. Not necessary a “good thing” for researchers, but important. setenv(TF_KERAS=1) # make sure we use python 3 . What are the possible ways to do that? May 12, 2019 · Instead of giving crude explanations this answer will provide links to great blog posts with much clearer explanation for the question. 1. Many NLP tasks are benefit from BERT to get the SOTA. These embeddings are added to the token embeddings before feeding them into the model. , 2019) are used as in-put for training. Embedding Layers in BERT. size sentence embeddings. the BiDAF model. Here’s a diagram from the… The main difference between the word embeddings of Word2vec, Glove, ELMo and BERT is that * Word2vec and Glove word embeddings are context independent- these models output just one vector (embedding) for each word, combining all the different sens I am planning to use BERT embeddings in the LSTM embedding layer instead of the usual Word2vec/Glove Embeddings. 4 Experimental Setup 4. The input representation for BERT: The input embeddings are the sum of the token embeddings, the segmentation embeddings and the position embeddings. bert-as-a-service is an open source project that provides BERT sentence embeddings optimized for production. Here, we utilize our proposed BERT word embeddings coupled with BERT sentence embeddings (a concatenation of the BERT word embeddings) to predict the presence of ADEs within a review. This breakthrough motivates our use of BERT embeddings on a standard BiDAF model for QA. BERT has the extra advantage that the same word can have completely different coordinates depending on the context. Sep 22, 2019 · We can use BERT for NLP tasks. You can very easily mix and match Flair, ELMo, BERT and classic word embeddings. It learns a unique embedding for the first and second sentences to help Mar 20, 2019 · Normally, BERT represents a general language modeling which supports transfer learning and fine-tuning on specific tasks, however, in this post we will only touch the feature extraction side of BERT by just extracting ELMo-like word embeddings from it, using Keras and TensorFlow. Table 3 shows our best results using the BERT embeddings along with results from related works. How to get sentence embedding using BERT? from transformers import BertTokenizer tokenizer=BertTokenizer. If you intrested to use ERNIE, just download tensorflow_ernie and load like BERT Embedding. To use the model in production, you need to consider factors such as latency, in addition to accuracy, which influences end user satisfaction with a service. I am planning to use BERT embeddings in the LSTM embedding layer instead of the usual Word2vec/Glove Embeddings. I want to do this for my LSTM model for detecting sentence semantic similarity. How BERT works BERT makes use of Transformer, an attention mechanism that learns contextual relations between words (or sub-words) in a text. Using the pre-trained BERT model¶. BERT also takes segment embeddings as input. NSS, June 4, 2017 . word-embeddings word2vec fasttext glove ELMo BERT language-models character-embeddings character-language-models neural-networks Since the work of Mikolov et al. May 14, 2019 · Why BERT embeddings? In this tutorial, we will use BERT to extract features, namely word and sentence embedding vectors, from text data. So, it allows to understanding a word based on its surroundings. There are 3 types of embedding layers in BERT:. BERT represents “sits” using both its left and right context — “The cat 31 Jan 2019 Word embedding plays a critical role in the realization of transfer learning word and document embeddings, including the BERT embeddings, 26 Feb 2019 In addition to beating previous performance benchmarks, using ELMo as a pre- trained embedding for other NLP tasks allows for a potential 10x . Use pytorch-transformers from hugging face to get bert embeddings in pytorch - get_bert_embeddings. To index into this table, you must use torch. Especially the BERT embeddings further improved the performance yielding new state-of-the-art results. Specifically, you learned: About word embeddings and that Keras supports word embeddings via the Embedding layer. Consequently, we investigate how readily useful the BERT embeddings are for generic models. Jul 22, 2019 · BERT Fine-Tuning Tutorial with PyTorch 22 Jul 2019. The module that allows you to use embeddings is torch. We take the output from the hidden states to generate new embeddings for each text input. Nov 02, 2018 · Making BERT Work for You The models that we are releasing can be fine-tuned on a wide variety of NLP tasks in a few hours or less. Also, BERT makes use of some special tokens (more general than words) like [CLS] 8 Aug 2019 Then, we apply one to BERT using TensorFlow Lite. In this publication, we present Sentence-BERT (SBERT), a modification of the pretrained BERT network that use siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be BERTEmbedding is based on keras-bert. All you need to do is instantiate each embedding you wish to combine and use them in a StackedEmbedding. You can use helper function extract_embeddings if the features of tokens or sentences (without further tuning) are what you need. e. Instead of using BERT to build an end-to-end model, using word representations from BERT can help you improve your model performance a lot, but save a lot of computing resources. . 1. Introduction. We can access this functionality with the following code: Note: A pre-trained model of BERT can also be used for generating text embeddings, similarly to many other feature-based models, such as doc2vec and ELMo. Why used learned positional embedding ? BERT approximately encodes syntax trees in the word embeddings it outputs for a sentence. I explained the power of using embeddings relies on the fact that they operate like coordinates in space. instead of GloVe embeddings, BERT wordpiece embeddings (Devlin et al. Copy PIP instructions If you want to use GPU, please import mxnet and set context 14 May 2019 In this tutorial, we will use BERT to extract features, namely word and What can we do with these word and sentence embedding vectors? 19 Mar 2019 Step by step tutorial to obtain contextualized token embeddings by Extract contextualized word embeddings from BERT using Keras and TF. the word "Queen" doesn't always produce the same vector, it'll be differ Jul 15, 2019 · This post discusses how we use BERT and similar self-attention architectures to address various text crunching tasks at Ether Labs. See why word embeddings are useful and how you can use pretrained word embeddings. In BERT, the embedding is the summation of three types of embeddings: where: Token Embeddings is a word vector, with the first word as the CLS flag, which can be used for classification tasks; How to use Word Embeddings Peng et al. When you use coordinates, as you do in the GPS system, it doesn’t matter how you name the thing (or what language you use to name it), it is still the same place. Before we start, have a look at the below examples. g. In this tutorial, the BERT model we will use is BERT BASE trained on an . The most commonly used approach is to average the BERT output layer (known as BERT embeddings) or by using the out-put of the first token (the [CLS] token). py. And provide it does - at the time that the BERT paper was published in 2018, BERT-based NLP models have surpassed the previous state-of-the-art results on eleven different NLP tasks, including Question-Answering. If no ADE was present in the review, a label of 0 was given. Neural Word Embeddings. A: position embeddings are limited to a max length (I forget the exact value, but you can find it in the paper), while there are only 2 sentence embeddings (E_A and E_B). Unclear if adding things on top of BERT really helps by very much. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. nn. Embedding, which takes two arguments: the vocabulary size, and the dimensionality of the embeddings. 2 Contextualized Word Embeddings The idea of modeling sentence or context-level se-mantics together with word-level semantics proved You’re probably thinking that switching to BERT is mostly the same as above. Empirical results from BERT are great, but biggest impact on the field is: With pre-training, bigger == better, without clear limits (so far). This would be no different in spirit from using Word2vec embeddings( for words not present in the 30k vocab we can create a word by adding up the sub word vectors) * We can use From what I've read in the BERT paper, you can use BERT to generate text embeddings and use those embeddings on your own model. What can we do with these word and sentence embedding vectors? First, these embeddings are useful for keyword/search expansion, semantic search and information retrieval. pip install bert-embedding. 29 Aug 2019 Because this method forces the model to learn how to use information In contrast, BERT trains a language model that takes both the previous 23 Jul 2019 I have used BERT NextSentencePredictor to find similar sentences or x = [1, 2, 3] ---> I will use BERT and word2vec embeddings here. BERT solves it with masking tokens in a sentence, leading to some deficiencies in comparison to XLnet. Normally, BERT represents a general language modeling which supports transfer learning and fine-tuning on specific tasks, however, in this post we will only touch the feature extraction side of BERT by just obtaining ELMo-like word embeddings from it, using Keras and TensorFlow. question answering and natural language inference). Use hyperparameter optimization to squeeze more performance out of your model. XLnet, to date, comes closest to the ideal of language modeling with a multilayered model leveraging off the entire sentence context for downstream tasks. fastText provides two models for computing word representations: skipgram and cbow ('continuous-bag-of-words'). , classification) are typically combined into a single step. A word bmbedding format generally tries to map a word using a dictionary to a vector. This means, embeddings can be cached on memory through DataFrames, can be saved on disk and shared as part of pipelines! We upgraded the TensorFlow version and also started using contrib LSTM Cells. 2 Attention Layer We use a focused, Context-Query attention layer on top of the pre-trained BERT embeddings identical to that of the QANet model. Jun 04, 2017 · An Intuitive Understanding of Word Embeddings: From Count Vectors to Word2Vec. It is possible to recover these trees by a linear transformation of the word embeddings. 23 Apr 2019 So I decided to make some visualizations of the embeddings, using a dimensional reduction algorithm called t-SNE. 16 Sep 2019 Sentence encoders such as Google's BERT and USE, Facebook's There are some work on using document embeddings for ranking BERT Wears GloVes: Distilling Static Embeddings from Pretrained for distilling contextual models into static embeddings; we apply our Introduction; Neural Word Embeddings; Amusing Word2vec Results; Advances in NLP: ElMO, BERT and GPT-2; Word2vec Use Cases; Foreign Languages embeddings for a given word depending on its context. Model size matters, even at huge scale. When you use coordinates, as You’re probably thinking that switching to BERT is mostly the same as above. Use BERT to get sentence and tokens embedding in an easier way BERT was one of the most exciting NLP papers published in 2018. Comparing XLnet to BERT, ELMo, and other unidirectional language models * BERT solves it wit Sep 17, 2019 · The input representation for BERT: The input embeddings are the sum of the token embeddings, the segmentation embeddings and the position embeddings. Thus, if a review was labeled to have an ADE, that review was given a label of 1. BERT is different from ELMo and company primarily because it targets a different training objective. Bert adds a special [CLS] token at the beginning of each sample/sentence. BERT frozen embeddings vs. Combining BERT and Flair. Work your way from a bag-of-words model with logistic regression to more advanced methods leading to convolutional neural networks. The main limitation of the earlier works is an inability to take into account both left and right contexts of the target word, since the language model objective is generated from left to right, adding successive words to a sentence. Revised on 12/13/19 to use the new transformers interface. Finally 4 Nov 2019 model of extractive-abstractive to combine BERT (Bidirectional Encoder Representations Secondly, we use BERT word embedding as text. On the other hand, the cbow model predicts the target word according to its context. We can access this functionality with the following code: The construction of BERT makes it unsuitable for semantic similarity search as well as for unsupervised tasks like clustering. Josh Taylor [5] has developed a semantic search engine using ELMo embeddings while Han Xiao [6] has developed Word Embeddings as well as Bert Embeddings are now annotators, just like any other component in the library. fine-tuning the BERT embedding for the downstream task. how to use bert embeddings</p> <figure id="attachment_26101" aria-describedby="caption-attachment-26101" style="width: 549px;" class="wp-caption aligncenter"><img class="size-full wp-image-26101" src="" alt="ZFS Get Compressratio And Compression" height="91" width="549"><figcaption id="caption-attachment-26101" class="wp-caption-text"></figcaption></figure></div> </div> <div class="comments td-container" id="comments"> <ol class="comment-list"> <li class="comment" id="comment-466613"> <div class="comment-meta" id="comment-466613"> </div> </li> <!-- #comment-## --> </ol> <div class="comment-pagination"> <div class="clearfix"></div> </div> <div id="respond" class="comment-respond"> <h3 id="reply-title" class="comment-reply-title">LEAVE A REPLY <small><span style="display: none;">Cancel reply</span></small></h3> <form action="" method="post" id="commentform" class="comment-form"> <div class="clearfix"></div> <div class="comment-form-input-wrap td-form-comment"> <textarea placeholder="Comment:" id="comment" name="comment" cols="45" rows="8" aria-required="true"></textarea> <div class="td-warning-comment">Please enter your comment!</div> </div> <div class="comment-form-input-wrap td-form-author"> <input class="" id="author" name="author" placeholder="Name:*" value="" size="30" aria-required="true" type="text"> <div class="td-warning-author">Please enter your name here</div> </div> <div class="comment-form-input-wrap td-form-email"> <input class="" id="email" name="email" placeholder="Email:*" value="" size="30" aria-required="true" type="text"> <div class="td-warning-email-error">You have entered an incorrect email address!</div> <div class="td-warning-email">Please enter your email address here</div> </div> <div class="comment-form-input-wrap td-form-url"> <input class="" id="url" name="url" placeholder="Website:" value="" size="30" type="text"> </div> <!-- Mailchimp for WordPress v4.7 - --><input name="_mc4wp_subscribe_wp-comment-form" value="0" type="hidden"> <p class="mc4wp-checkbox mc4wp-checkbox-wp-comment-form"><label><input name="_mc4wp_subscribe_wp-comment-form" value="1" type="checkbox"><span>Sign me up for the STH newsletter!</span></label></p> <!-- / Mailchimp for WordPress --> <p class="form-submit"><input name="submit" id="submit" class="submit" value="Post Comment" type="submit"> <input name="comment_post_ID" value="26097" id="comment_post_ID" type="hidden"> <input name="comment_parent" id="comment_parent" value="0" type="hidden"> </p> <p style="display: none;"><input id="akismet_comment_nonce" name="akismet_comment_nonce" value="205a5fe305" type="hidden"></p> <div class="inv-recaptcha-holder"></div> <p style="display: none;"><input id="ak_js" name="ak_js" value="36" type="hidden"></p> </form> </div> <!-- #respond --> </div> <!-- /.content --> <!-- footer AD --> <div class="td-a-rec td-a-rec-id-footer_mob"> <!-- STH Main Site Mobile Bottom Ad --> <ins class="adsbygoogle" style="display: block;" data-ad-client="ca-pub-6892283444156700" data-ad-slot="8976002122" data-ad-format="auto"></ins> </div> <!-- footer --> <div class="td-mobile-footer-wrap"> <div class="td-container"> <div class="td-footer-wrap"><aside class="td-footer-logo"><img class="td-retina-data" src="" data-retina="" alt="STH" title="STH"></aside></div> <div class="td-footer-wrap"><aside class="td-footer-description"></aside></div> </div> <!-- close td-container --> </div> <!-- close footer --> <!-- sub footer --> <div class="td-mobile-sub-footer-wrap"> <div class="td-container"> <div class="td-sub-footer-menu"> </div> <div class="td-sub-footer-copy"> </div> </div> </div> <!-- close td-outer-wrap --> <!-- Theme: Newspaper by tagDiv 2017 Version: (rara) Deploy mode: deploy uid: 5df1ce5a9c8b5 --> </body> </html>
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