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Hugging Face


Hugging Face


Product Overview.

Hugging Face – The AI community building the future

Build, train and deploy state-of-the-art models powered by the reference open-source in natural language processing.

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About the Company.

Solving NLP, one commit at a time.

BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives:

Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence.

Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not.

This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs.

Other Products. is one singular product. The product offers each of the following for your convenience:

Tasks you can run: All Transformers pipelines available: ASR, feature extraction, text classification, NER, question answering, translation, summarization, text generation, zero-shot classification, conversational AI, table question answering.

Scalability: They built our infrastructure to support real-time consumer use cases and scale automatically as usage grows to support up to 1,000 requests per second.

NLP: With over 10,000 models trained in over 160 languages, Hugging Face offers the largest and most diverse library of state of the art models, and the Inference API makes them all available to you via simple API calls.

Latency: They accelerate their models on CPU and GPU so your apps work faster.

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