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Oct 05, 2020 · Build & Deploy BERT, DistilBERT, FastText NLP Models in Production with Flask, uWSGI, and X at AWS EC2. Read more... Video Tutorials → Learn SQL Reporting Services Beginning Report Training Webpack pdf loader
Github repo with tutorials to fine tune transformers for diff NLP tasks. DistilBERT Question Answering model using SQuAD served with Flask, deployed on Google Cloud Run.

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This tutorial will take you through several examples of using 🤗 Transformers models with your own datasets. The guide shows one of many valid workflows for using these models and is meant to be illustrative rather than definitive.

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Every decent distillery should have a guided tour - no free samples on this one though! In the video you can see the machine automatically pumping in the homebrewed cider...

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Recently, the researchers at Amazon introduced an optimal subset of the popular BERT architecture for neural architecture search. This smaller version of BERT is known as BORT and is able to be pre-trained in 288 GPU hours, which is 1.2% of the time required to pre-train the highest-performing BERT parametric architectural variant, RoBERTa-large.

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See Tutorial 1 on how to go on with indexing your docs. SQL / InMemory (Alternative) haystack.database.sql.SQLDocumentStore & haystack.database.memory.InMemoryDocumentStore. These DocumentStores are mainly intended to simplify the first development steps or test a prototype on an existing SQL Database containing your texts.

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Sentiment analysis neural network trained by fine-tuning BERT, ALBERT, or DistilBERT on the Stanford Sentiment Treebank. What’s new in matplotlib 3.3.0; scispacy – A full spaCy pipeline and models for scientific/biomedical documents. Wikipedia is a Python library that makes it easy to access and parse data from Wikipedia. pygooglenews

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TextAttack Model Zoo¶. TextAttack is model-agnostic - meaning it can run attacks on models implemented in any deep learning framework. Model objects must be able to take a string (or list of strings) and return an output that can be processed by the goal function.

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DistilBERT (Sanh et al.,2019) Specialty: Multilingual XLM/RoBERTa (Lample and Conneau,2019b) 7UDQVIRUPHU 7RNHQL]HU +HDG +HDG Tokenizers Name Ex. Uses Character-Level BPE NMT, GPT Byte-Level BPE GPT-2 WordPiece BERT SentencePiece XLNet Unigram LM Character Reformer Custom Bio-Chem Figure 2: The Transformers library.

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