Smaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT
Blog by Victor Sanh : https://medium.com/huggingface/distilbert-8cf3380435b5
#MachineLearning #NLP #Bert #Distillation #Transformers
Blog by Victor Sanh : https://medium.com/huggingface/distilbert-8cf3380435b5
#MachineLearning #NLP #Bert #Distillation #Transformers
Medium
🏎 Smaller, faster, cheaper, lighter: Introducing DilBERT, a distilled version of BERT
You can find the code to reproduce the training of DilBERT along with pre-trained weights for DilBERT here.
Smaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT
Blog by Victor Sanh : https://medium.com/huggingface/distilbert-8cf3380435b5
#MachineLearning #NLP #Bert #Distillation #Transformers
Blog by Victor Sanh : https://medium.com/huggingface/distilbert-8cf3380435b5
#MachineLearning #NLP #Bert #Distillation #Transformers
Medium
🏎 Smaller, faster, cheaper, lighter: Introducing DilBERT, a distilled version of BERT
You can find the code to reproduce the training of DilBERT along with pre-trained weights for DilBERT here.
Most common libraries for Natural Language Processing:
CoreNLP from Stanford group:
http://stanfordnlp.github.io/CoreNLP/index.html
NLTK, the most widely-mentioned NLP library for Python:
http://www.nltk.org/
TextBlob, a user-friendly and intuitive NLTK interface:
https://textblob.readthedocs.io/en/dev/index.html
Gensim, a library for document similarity analysis:
https://radimrehurek.com/gensim/
SpaCy, an industrial-strength NLP library built for performance:
https://spacy.io/docs/
Source: https://itsvit.com/blog/5-heroic-tools-natural-language-processing/
#nlp #digest #libs
CoreNLP from Stanford group:
http://stanfordnlp.github.io/CoreNLP/index.html
NLTK, the most widely-mentioned NLP library for Python:
http://www.nltk.org/
TextBlob, a user-friendly and intuitive NLTK interface:
https://textblob.readthedocs.io/en/dev/index.html
Gensim, a library for document similarity analysis:
https://radimrehurek.com/gensim/
SpaCy, an industrial-strength NLP library built for performance:
https://spacy.io/docs/
Source: https://itsvit.com/blog/5-heroic-tools-natural-language-processing/
#nlp #digest #libs
CoreNLP
High-performance human language analysis tools, now with native deep learning modules in Python, available in many human languages.
Write With Transformer
Hugging Face released a new version of their Write With Transformer app, using a language model trained directly on Arxiv to generate Deep Learning and NLP completions!
In addition, they add state-of-the-art NLP models such as GPT, GPT-2 and XLNet completions:
https://transformer.huggingface.co/
H / T : Lysandre Debut
#Transformer #Pytorch #NLP
@ArtificialIntelligenceArticles
Hugging Face released a new version of their Write With Transformer app, using a language model trained directly on Arxiv to generate Deep Learning and NLP completions!
In addition, they add state-of-the-art NLP models such as GPT, GPT-2 and XLNet completions:
https://transformer.huggingface.co/
H / T : Lysandre Debut
#Transformer #Pytorch #NLP
@ArtificialIntelligenceArticles
banana-projects-transformer-autocomplete.hf.space
Write With Transformer
See how a modern neural network auto-completes your text
Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets
Geva et al.: https://arxiv.org/abs/1908.07898
#ArtificialIntelligence #MachineLearning #NLP
Geva et al.: https://arxiv.org/abs/1908.07898
#ArtificialIntelligence #MachineLearning #NLP
arXiv.org
Are We Modeling the Task or the Annotator? An Investigation of...
Crowdsourcing has been the prevalent paradigm for creating natural language understanding datasets in recent years. A common crowdsourcing practice is to recruit a small number of high-quality...
What Kind of Language Is Hard to Language-Model?
Mielke et al.: https://arxiv.org/abs/1906.04726
#ArtificialIntelligence #MachineLearning #NLP
Mielke et al.: https://arxiv.org/abs/1906.04726
#ArtificialIntelligence #MachineLearning #NLP
Transformers: State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch
By 🤗 Hugging Face : https://huggingface.co/transformers
#Transformers #MachineLearning #NLP
By 🤗 Hugging Face : https://huggingface.co/transformers
#Transformers #MachineLearning #NLP
Extreme Language Model Compression with Optimal Subwords and Shared Projections
Zhao et al.: https://arxiv.org/abs/1909.11687
#neuralnetwork #bert #nlp
Zhao et al.: https://arxiv.org/abs/1909.11687
#neuralnetwork #bert #nlp
arXiv.org
Extremely Small BERT Models from Mixed-Vocabulary Training
Pretrained language models like BERT have achieved good results on NLP tasks, but are impractical on resource-limited devices due to memory footprint. A large fraction of this footprint comes from...
The State of Transfer Learning in NLP
By Sebastian Ruder : http://ruder.io/state-of-transfer-learning-in-nlp/
#TransferLearning #NaturalLanguageProcessing #NLP
By Sebastian Ruder : http://ruder.io/state-of-transfer-learning-in-nlp/
#TransferLearning #NaturalLanguageProcessing #NLP
ruder.io
The State of Transfer Learning in NLP
This post expands on the NAACL 2019 tutorial on Transfer Learning in NLP. It highlights key insights and takeaways and provides updates based on recent work.
Q8BERT: Quantized 8Bit BERT
Zafrir et al.: https://arxiv.org/abs/1910.06188
#NaturalLanguageProcessing #NLP #Transformer
Zafrir et al.: https://arxiv.org/abs/1910.06188
#NaturalLanguageProcessing #NLP #Transformer
arXiv.org
Q8BERT: Quantized 8Bit BERT
Recently, pre-trained Transformer based language models such as BERT and GPT, have shown great improvement in many Natural Language Processing (NLP) tasks. However, these models contain a large...