Remove Auto-classification Remove BERT Remove Python
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Amazon EC2 DL2q instance for cost-efficient, high-performance AI inference is now generally available

AWS Machine Learning Blog

Model category Number of models Examples​ NLP​ 157 BERT, BART, FasterTransformer, T5, Z-code MOE Generative AI – NLP 40 LLaMA, CodeGen, GPT, OPT, BLOOM, Jais, Luminous, StarCoder, XGen Generative AI – Image 3 Stable diffusion v1.5 Set up the environment and install required packages Install Python 3.8. Set up the Python 3.8

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Accelerate hyperparameter grid search for sentiment analysis with BERT models using Weights & Biases, Amazon EKS, and TorchElastic

AWS Machine Learning Blog

Transformer-based language models such as BERT ( Bidirectional Transformers for Language Understanding ) have the ability to capture words or sentences within a bigger context of data, and allow for the classification of the news sentiment given the current state of the world. The code can be found on the GitHub repo. eks-create.sh

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Adapting language-based models beyond English

Snorkel AI

For text classification, however, there are many similarities. Snorkel Flow’s “Auto-Suggest Key Terms” feature works on any language with “white-space” tokenization. The following image shows an auto-suggestion from a Spanish Sentiment dataset (“ mucha suerte” translates to “good luck”).

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Simplify Deployment and Monitoring of Foundation Models with DataRobot MLOps

DataRobot Blog

Then you can use the model to perform tasks such as text generation, classification, and translation. As an example, getting started with a BERT model for question answering (bert-large-uncased-whole-word-masking-finetuned-squad) is as easy as executing these lines: !pip pip install transformers==4.25.1 datarobot==3.0.2

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Fine-tune GPT-J using an Amazon SageMaker Hugging Face estimator and the model parallel library

AWS Machine Learning Blog

It can support a wide variety of use cases, including text classification, token classification, text generation, question and answering, entity extraction, summarization, sentiment analysis, and many more. Use the SageMaker model parallel library The SageMaker model parallel library comes with the SageMaker Python SDK.

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Fine-tune a BGE embedding model using synthetic data from Amazon Bedrock

AWS Machine Learning Blog

It is a family of embedding models with a BERT-like architecture, designed to produce high-quality embeddings from text data. TEI is a high-performance toolkit for deploying and serving popular text embeddings and sequence classification models, including support for FlagEmbedding models. GB, 1,024 embedding dimensions bge-base-en-v1.5:

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Introducing spaCy v3.0

Explosion

de_dep_news_trf German bert-base-german-cased 99.0 95.8 - es_dep_news_trf Spanish bert-base-spanish-wwm-cased 98.2 94.4 - zh_core_web_trf Chinese bert-base-chinese 92.5 The config can be loaded as a Python dict. In your custom architectures, you can use Python type hints to tell the config which types of data to expect.

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