Simple, Scalable Adaptation for Neural Machine Translation
Fine-tuning pre-trained Neural Machine Translation (NMT) models is the dominant approach for adapting to new languages and domains. However, fine-tuning requires adapting and maintaining a separate model for each target task. Researchers from Google propose a simple yet efficient approach for adaptation in #NMT. Their proposed approach consists of injecting tiny task specific adapter layers into a pre-trained model. These lightweight adapters, with just a small fraction of the original model size, adapt the model to multiple individual tasks simultaneously.
Guess it can be applied not only in #NMT but in many other #NLP, #NLU and #NLG tasks.
Paper: https://arxiv.org/pdf/1909.08478.pdf
#BERT #NMT #FineTuning
Fine-tuning pre-trained Neural Machine Translation (NMT) models is the dominant approach for adapting to new languages and domains. However, fine-tuning requires adapting and maintaining a separate model for each target task. Researchers from Google propose a simple yet efficient approach for adaptation in #NMT. Their proposed approach consists of injecting tiny task specific adapter layers into a pre-trained model. These lightweight adapters, with just a small fraction of the original model size, adapt the model to multiple individual tasks simultaneously.
Guess it can be applied not only in #NMT but in many other #NLP, #NLU and #NLG tasks.
Paper: https://arxiv.org/pdf/1909.08478.pdf
#BERT #NMT #FineTuning
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
It's the method for pre-training seq2seq models by de-noising text.
BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
They evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token.
BART matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, Q&A, and summarization tasks, with gains of up to 6 ROUGE.
Paper: https://arxiv.org/abs/1910.13461
#nlp #bert
It's the method for pre-training seq2seq models by de-noising text.
BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
They evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token.
BART matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, Q&A, and summarization tasks, with gains of up to 6 ROUGE.
Paper: https://arxiv.org/abs/1910.13461
#nlp #bert
Revealing the Dark Secrets of BERT
This work interpretation of self-attention.
Using a subset of GLUE tasks and a set of handcrafted features-of-interest, they proposed the methodology and carry out a qualitative and quantitative analysis of the information encoded by the individual BERT’s heads.
The findings suggest that there is a limited set of attention patterns that are repeated across different heads, indicating the overall model overparametrization.
Also, show that manually disabling attention in certain heads leads to a performance improvement over the regular fine-tuned BERT models.
paper: https://arxiv.org/abs/1908.08593
#nlp #bert
This work interpretation of self-attention.
Using a subset of GLUE tasks and a set of handcrafted features-of-interest, they proposed the methodology and carry out a qualitative and quantitative analysis of the information encoded by the individual BERT’s heads.
The findings suggest that there is a limited set of attention patterns that are repeated across different heads, indicating the overall model overparametrization.
Also, show that manually disabling attention in certain heads leads to a performance improvement over the regular fine-tuned BERT models.
paper: https://arxiv.org/abs/1908.08593
#nlp #bert
Unsupervised Cross-lingual Representation Learning at Scale
They release XLM-R, a Transformer MLM trained in 100 langs on 2.5 TB of text data! Which obtains state-of-the-art performance on cross-lingual classification, sequence labeling and question answering.
Introduced a comprehensive analysis of the capacity and limits of unsupervised multilingual masked language modeling at scale.
XLM-R especially outperforms mBERT and XLM-100 on low-resource languages, for which CommonCrawl data enables representation learning: +13.7% and +9.3% for Urdu, +21.6% and +13.8% accuracy for Swahili on XNLI.
Soon on transformers by huggingface repo & at tf.hub
paper: https://arxiv.org/abs/1911.02116
code: https://github.com/pytorch/fairseq/tree/master/examples/xlmr
#nlp #bert #xlu #transformer
They release XLM-R, a Transformer MLM trained in 100 langs on 2.5 TB of text data! Which obtains state-of-the-art performance on cross-lingual classification, sequence labeling and question answering.
Introduced a comprehensive analysis of the capacity and limits of unsupervised multilingual masked language modeling at scale.
XLM-R especially outperforms mBERT and XLM-100 on low-resource languages, for which CommonCrawl data enables representation learning: +13.7% and +9.3% for Urdu, +21.6% and +13.8% accuracy for Swahili on XNLI.
Soon on transformers by huggingface repo & at tf.hub
paper: https://arxiv.org/abs/1911.02116
code: https://github.com/pytorch/fairseq/tree/master/examples/xlmr
#nlp #bert #xlu #transformer
CamemBERT
New state-of-the-art in French NLU 🇫🇷
CamemBERT is a state-of-the-art language model for French based on the RoBERTa architecture pretrained on the French subcorpus of the newly available multilingual corpus OSCAR.
Project page & examples: https://camembert-model.fr/
#nlp #bert #lm
New state-of-the-art in French NLU 🇫🇷
CamemBERT is a state-of-the-art language model for French based on the RoBERTa architecture pretrained on the French subcorpus of the newly available multilingual corpus OSCAR.
Project page & examples: https://camembert-model.fr/
#nlp #bert #lm
CamemBERT
A Tasty French Language Model
A Visual Guide to Using BERT for the First Time
A new blog post and notebook by Jay Alammar to get you started with using a pre-trained BERT model for the first time. It uses huggingface libs for sentence embedding and scikitLearn for classification
blog: https://jalammar.github.io/a-visual-guide-to-using-bert-for-the-first-time
#nlp #bert
A new blog post and notebook by Jay Alammar to get you started with using a pre-trained BERT model for the first time. It uses huggingface libs for sentence embedding and scikitLearn for classification
blog: https://jalammar.github.io/a-visual-guide-to-using-bert-for-the-first-time
#nlp #bert
FreeLB: Enhanced Adversarial Training for Language Understanding
The authors propose a novel adversarial training algorithm – FreeLB, that promotes higher robustness and invariance in the embedding space, by adding adversarial perturbations to word embeddings and minimizing the resultant adversarial risk inside different regions around input samples, applied to Transformer-based models for NLU & commonsense reasoning tasks.
Experiments on the GLUE benchmark show that when applied only to the finetuning stage, it is able to improve the overall test scores:
* of BERT-based model from 78.3 -> 79.4
* RoBERTa-large model from 88.5 -> 88.8
The proposed approach achieves SOTA single-model test accuracies of 85.44% and 67.75% on ARC-Easy and ARC-Challenge.
paper: https://arxiv.org/abs/1909.11764
#nlp #nlu #bert #adversarial #ICLR
The authors propose a novel adversarial training algorithm – FreeLB, that promotes higher robustness and invariance in the embedding space, by adding adversarial perturbations to word embeddings and minimizing the resultant adversarial risk inside different regions around input samples, applied to Transformer-based models for NLU & commonsense reasoning tasks.
Experiments on the GLUE benchmark show that when applied only to the finetuning stage, it is able to improve the overall test scores:
* of BERT-based model from 78.3 -> 79.4
* RoBERTa-large model from 88.5 -> 88.8
The proposed approach achieves SOTA single-model test accuracies of 85.44% and 67.75% on ARC-Easy and ARC-Challenge.
paper: https://arxiv.org/abs/1909.11764
#nlp #nlu #bert #adversarial #ICLR
Towards Lingua Franca Named Entity Recognition with BERT
The authors present a simple and effective recipe for building #multilingual #NER systems with #BERT.
By utilizing a multilingual BERT framework, they were able to not only train a system that can perform inference on English, German, Spanish, and Dutch languages, but it performs better than the same model trained only on one language at a time, and also is able to perform 0-shot inference.
The resulting model yields #SotA results on CoNLL Spanish and Dutch, and on OntoNotes Chinese and Arabic datasets.
Also, the English trained model yields SotA results for 0-shot languages for Spanish, Dutch, and German NER, improving it by a range of 2.4F to 17.8F.
Furthermore, the runtime signature (memory/CPU/GPU) of the model is the same as the models built on single languages, significantly simplifying its life- cycle maintenance.
paper: https://arxiv.org/abs/1912.01389
The authors present a simple and effective recipe for building #multilingual #NER systems with #BERT.
By utilizing a multilingual BERT framework, they were able to not only train a system that can perform inference on English, German, Spanish, and Dutch languages, but it performs better than the same model trained only on one language at a time, and also is able to perform 0-shot inference.
The resulting model yields #SotA results on CoNLL Spanish and Dutch, and on OntoNotes Chinese and Arabic datasets.
Also, the English trained model yields SotA results for 0-shot languages for Spanish, Dutch, and German NER, improving it by a range of 2.4F to 17.8F.
Furthermore, the runtime signature (memory/CPU/GPU) of the model is the same as the models built on single languages, significantly simplifying its life- cycle maintenance.
paper: https://arxiv.org/abs/1912.01389
Episodic Memory in Lifelong Language Learning
tl;dr – the model needs to learn from a stream of text examples without any dataset identifier.
The authors propose an episodic memory model that performs sparse experience replay and local adaptation to mitigate catastrophic forgetting in this setup. Experiments on text classification and question answering demonstrate the complementary benefits of sparse experience replay & local adaptation to allow the model to continuously learn from new datasets.
Also, they show that the space complexity of the episodic memory module can be reduced significantly (∼50-90%) by randomly choosing which examples to store in memory with a minimal decrease in performance. They consider an episodic memory component as a crucial building block of general linguistic intelligence and see the model as the first step in that direction.
paper: https://arxiv.org/abs/1906.01076
#nlp #bert #NeurIPSConf19
tl;dr – the model needs to learn from a stream of text examples without any dataset identifier.
The authors propose an episodic memory model that performs sparse experience replay and local adaptation to mitigate catastrophic forgetting in this setup. Experiments on text classification and question answering demonstrate the complementary benefits of sparse experience replay & local adaptation to allow the model to continuously learn from new datasets.
Also, they show that the space complexity of the episodic memory module can be reduced significantly (∼50-90%) by randomly choosing which examples to store in memory with a minimal decrease in performance. They consider an episodic memory component as a crucial building block of general linguistic intelligence and see the model as the first step in that direction.
paper: https://arxiv.org/abs/1906.01076
#nlp #bert #NeurIPSConf19
Cross-Lingual Ability of Multilingual BERT: An Empirical Study to #ICLR2020
In this work, the authors provide a comprehensive study of the contribution of different components in multilingual #BERT (M-BERT) to its cross-lingual ability.
They study the impact of linguistic properties of the languages, the architecture of the model, and the learning objectives. The experimental study is done in the context of three typologically different languages – #Spanish, #Hindi, & #Russian – & using two conceptually different #NLP tasks, textual entailment & #NER.
Also, they construct a new corpus – Fake-English (#enfake), by shifting the Unicode of each character in English Wikipedia text by a large constant so that there is strictly no character overlap with any other Wikipedia text.
And, in this work, they consider Fake-English as a different language.
Among their key conclusions are the fact that the lexical overlap between languages plays a negligible role in the cross-lingual success, while the depth of the network is an integral part of it.
paper: https://arxiv.org/abs/1912.07840
In this work, the authors provide a comprehensive study of the contribution of different components in multilingual #BERT (M-BERT) to its cross-lingual ability.
They study the impact of linguistic properties of the languages, the architecture of the model, and the learning objectives. The experimental study is done in the context of three typologically different languages – #Spanish, #Hindi, & #Russian – & using two conceptually different #NLP tasks, textual entailment & #NER.
Also, they construct a new corpus – Fake-English (#enfake), by shifting the Unicode of each character in English Wikipedia text by a large constant so that there is strictly no character overlap with any other Wikipedia text.
And, in this work, they consider Fake-English as a different language.
Among their key conclusions are the fact that the lexical overlap between languages plays a negligible role in the cross-lingual success, while the depth of the network is an integral part of it.
paper: https://arxiv.org/abs/1912.07840
Revealing the Dark Secrets of BERT
tl;dr:
* BERT is heavily over parametrized
* BERT does not need to be all that smart
* BERT’s success is due to ~black magic~ something other than self-attention
This work focuses on the complementary question: what happens in the fine-tuned #BERT? In particular, how much of the linguistically interpretable self-attention patterns that are presumed to be its strength are actually used to solve downstream tasks?
Using a subset of #GLUE tasks and a set of handcrafted features-of-interest, they propose the methodology and carry out a qualitative and quantitative analysis of the information encoded by the individual BERT’s heads.
Also, they show that manually disabling attention in certain heads leads to performance improvement over the regular fine-tuned BERT models.
paper: https://www.aclweb.org/anthology/D19-1445.pdf
blog post: https://text-machine-lab.github.io/blog/2020/bert-secrets/
tl;dr:
* BERT is heavily over parametrized
* BERT does not need to be all that smart
* BERT’s success is due to ~black magic~ something other than self-attention
This work focuses on the complementary question: what happens in the fine-tuned #BERT? In particular, how much of the linguistically interpretable self-attention patterns that are presumed to be its strength are actually used to solve downstream tasks?
Using a subset of #GLUE tasks and a set of handcrafted features-of-interest, they propose the methodology and carry out a qualitative and quantitative analysis of the information encoded by the individual BERT’s heads.
Also, they show that manually disabling attention in certain heads leads to performance improvement over the regular fine-tuned BERT models.
paper: https://www.aclweb.org/anthology/D19-1445.pdf
blog post: https://text-machine-lab.github.io/blog/2020/bert-secrets/
Microsoft open sources breakthrough optimizations for transformer inference on GPU and CPU
How?
#Transformer models like #BERT consist of a graph of many operators. Graph optimization, ranging from small graph simplifications and node eliminations to more complex node fusions and layout optimizations, is an essential technique built into #ONNX Runtime.
Since the BERT model is mainly composed of stacked transformer cells, we optimize each cell by fusing key sub-graphs of multiple elementary operators into single kernels for both CPU and GPU, including Self-Attention, LayerNormalization, and Gelu layers. This significantly reduces memory copy between numerous elementary computations.
Additionally, in the CPU implementation of Self-Attention, the columns of matrix Q, K, and V are partitioned based on the number of self-attention heads. With this optimization, we can significantly increase the parallelization and fully leverage available CPU cores. Moreover, the transpose op following the full connection of Q, K, and V can be computed within GEMM, which further reduces the computation cost.
blog post: https://cloudblogs.microsoft.com/opensource/2020/01/21/microsoft-onnx-open-source-optimizations-transformer-inference-gpu-cpu/
How?
#Transformer models like #BERT consist of a graph of many operators. Graph optimization, ranging from small graph simplifications and node eliminations to more complex node fusions and layout optimizations, is an essential technique built into #ONNX Runtime.
Since the BERT model is mainly composed of stacked transformer cells, we optimize each cell by fusing key sub-graphs of multiple elementary operators into single kernels for both CPU and GPU, including Self-Attention, LayerNormalization, and Gelu layers. This significantly reduces memory copy between numerous elementary computations.
Additionally, in the CPU implementation of Self-Attention, the columns of matrix Q, K, and V are partitioned based on the number of self-attention heads. With this optimization, we can significantly increase the parallelization and fully leverage available CPU cores. Moreover, the transpose op following the full connection of Q, K, and V can be computed within GEMM, which further reduces the computation cost.
blog post: https://cloudblogs.microsoft.com/opensource/2020/01/21/microsoft-onnx-open-source-optimizations-transformer-inference-gpu-cpu/
A new approach for NER on partially labeled datasets.
One of the common problems with NER modeling is the lack of datasets covering all required slot types. Often there are several datasets that have labels for different entities.
The key idea of the paper is using multi-task transformer-based architecture on multiple datasets.
The model architecture looks like this:
- lexicon encoder layer (input is tokens with words, position and segment embeddings);
- transformer encoder, which generates the shared contextual embedding vectors;
- separate heads for each dataset.
During the training phase, it is necessary to not only train the task-dependent layers but also to fine-tune the shared language model.
Experiments were conducted on four datasets using a single Tesla K80. A single multi-task model (iterating over datasets) shows SOTA results and trains faster than separate models for each task.
Paper: https://arxiv.org/abs/2001.08904
#nlp #bert #ner #biomedical
MT-BioNER: Multi-task Learning for Biomedical Named EntityRecognition using Deep Bidirectional TransformersA new approach for NER on partially labeled datasets.
One of the common problems with NER modeling is the lack of datasets covering all required slot types. Often there are several datasets that have labels for different entities.
The key idea of the paper is using multi-task transformer-based architecture on multiple datasets.
The model architecture looks like this:
- lexicon encoder layer (input is tokens with words, position and segment embeddings);
- transformer encoder, which generates the shared contextual embedding vectors;
- separate heads for each dataset.
During the training phase, it is necessary to not only train the task-dependent layers but also to fine-tune the shared language model.
Experiments were conducted on four datasets using a single Tesla K80. A single multi-task model (iterating over datasets) shows SOTA results and trains faster than separate models for each task.
Paper: https://arxiv.org/abs/2001.08904
#nlp #bert #ner #biomedical
BERT-of-Theseus: Compressing BERT by Progressive Module Replacing
tl;dr
with a huggingface – compatible weights
take original BERT, replace some of his layers with new (smaller) ones randomly during the distillation. the probability of replacing the module will increase over time, resulting in a small model at the end.
them approach leverages only one loss function and one hyper-parameter, liberating human effort from hyper-parameter tuning.
also, they outperform existing knowledge distillation approaches on GLUE benchmark, showing a new perspective of model compression
paper: https://arxiv.org/abs/2002.02925
github: https://github.com/JetRunner/BERT-of-Theseus
#nlp #compressing #knowledge #distillation #bert
tl;dr
[ONE loss] + [ONE hyperparameter] + [NO external data] = GREAT PERFORMANCEwith a huggingface – compatible weights
take original BERT, replace some of his layers with new (smaller) ones randomly during the distillation. the probability of replacing the module will increase over time, resulting in a small model at the end.
them approach leverages only one loss function and one hyper-parameter, liberating human effort from hyper-parameter tuning.
also, they outperform existing knowledge distillation approaches on GLUE benchmark, showing a new perspective of model compression
paper: https://arxiv.org/abs/2002.02925
github: https://github.com/JetRunner/BERT-of-Theseus
#nlp #compressing #knowledge #distillation #bert
Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space
The authors propose the first large-scale language VAE model – Optimus.
This new model uses BERT weights in the encoder and GPT-2 weights in the decoder. Thanks to this Optimus supports NLU and text generation tasks. Learned language representation is more universal, which means that it is easier to fine-tune this model to a new domain/task. Also, Optimus can control high-level semantics in text generation (tense, topic, sentiment).
There are several novel contributions, which are made thanks to this work:
– latent vector injection: two schemes are suggested to inject conditioning vectors into GPT-2 without retraining it;
– the idea to combine BERT and GPT-2 could inspire people to integrate existing language models into larger and ever more complex models;
– pre-training on a big corpora is an effective approach to reduce KL vanishing;
– VAE is a good approach to balance the compactness and usability of learned representations;
– pre-training latent space improves performance on several language tasks;
Experimental results on a wide range of tasks and datasets have demonstrated the strong performance of OPTIMUS, including new state-of-the-art for language VAEs.
Paper: https://arxiv.org/abs/2004.04092v1
Github: https://github.com/ChunyuanLI/Optimus
#deeplearning #nlp #nlu #transformer #vae #bert #gpt2
The authors propose the first large-scale language VAE model – Optimus.
This new model uses BERT weights in the encoder and GPT-2 weights in the decoder. Thanks to this Optimus supports NLU and text generation tasks. Learned language representation is more universal, which means that it is easier to fine-tune this model to a new domain/task. Also, Optimus can control high-level semantics in text generation (tense, topic, sentiment).
There are several novel contributions, which are made thanks to this work:
– latent vector injection: two schemes are suggested to inject conditioning vectors into GPT-2 without retraining it;
– the idea to combine BERT and GPT-2 could inspire people to integrate existing language models into larger and ever more complex models;
– pre-training on a big corpora is an effective approach to reduce KL vanishing;
– VAE is a good approach to balance the compactness and usability of learned representations;
– pre-training latent space improves performance on several language tasks;
Experimental results on a wide range of tasks and datasets have demonstrated the strong performance of OPTIMUS, including new state-of-the-art for language VAEs.
Paper: https://arxiv.org/abs/2004.04092v1
Github: https://github.com/ChunyuanLI/Optimus
#deeplearning #nlp #nlu #transformer #vae #bert #gpt2
Transformer Reasoning Network for Image-Text Matching and Retrieval
A new approach for image-text matching using Faster-RCNN Bottom-Up and BERT.
Usually, downstream applications use the ResNet or one of its variants as the backbone CNN. Its simple and modular design can be easily adapted to various tasks. However, since ResNet models are originally designed for image classification, they may not be suitable for various downstream applications because of the limited receptive-field size and lack of cross-channel interaction.
Authors suggest an architecture, where images and texts are processed at first, and then their representations are combined.
Main contributions of the paper:
- TERN Architecture
- NDCG metric in addition to Recall@K
- show SOTA result on the benchmark
Paper: https://arxiv.org/abs/2004.09144
Code: https://github.com/mesnico/TERN
#computervision #deeplearning #bert #imagetextmatching
A new approach for image-text matching using Faster-RCNN Bottom-Up and BERT.
Usually, downstream applications use the ResNet or one of its variants as the backbone CNN. Its simple and modular design can be easily adapted to various tasks. However, since ResNet models are originally designed for image classification, they may not be suitable for various downstream applications because of the limited receptive-field size and lack of cross-channel interaction.
Authors suggest an architecture, where images and texts are processed at first, and then their representations are combined.
Main contributions of the paper:
- TERN Architecture
- NDCG metric in addition to Recall@K
- show SOTA result on the benchmark
Paper: https://arxiv.org/abs/2004.09144
Code: https://github.com/mesnico/TERN
#computervision #deeplearning #bert #imagetextmatching
SpERT: Span-based Joint Entity and Relation Extraction with Transformer Pre-training
Authors introduce SpERT, an attention model for span-based joint entity and relation extraction.
This work investigates the use of Transformer networks for relation extraction: given a pre-defined set of target relations and a sentence such as “Leonardo DiCaprio starred in Christopher Nolan’s thriller Inception”, the goal is to extract triplets such as (“Leonardo DiCaprio”, Plays-In, “Inception”) or (“Inception”, Director, “Christopher Nolan”).
The main contributions of the paper are:
– a novel approach towards span-based joint entity and relation extraction
– ablation study showing that negative samples from the same sentence yield efficient training, a localized context representation is beneficial, finetuning a pre-trained model yields a strong performance increase over training from scratch.
This approach improves the SOTA score on CoNLL04 dataset by 2.6% (micro) F1.
Paper: https://arxiv.org/abs/1909.07755
Code: https://github.com/markus-eberts/spert
#nlp #deeplearning #transformer #bert #ner #relationextraction
Authors introduce SpERT, an attention model for span-based joint entity and relation extraction.
This work investigates the use of Transformer networks for relation extraction: given a pre-defined set of target relations and a sentence such as “Leonardo DiCaprio starred in Christopher Nolan’s thriller Inception”, the goal is to extract triplets such as (“Leonardo DiCaprio”, Plays-In, “Inception”) or (“Inception”, Director, “Christopher Nolan”).
The main contributions of the paper are:
– a novel approach towards span-based joint entity and relation extraction
– ablation study showing that negative samples from the same sentence yield efficient training, a localized context representation is beneficial, finetuning a pre-trained model yields a strong performance increase over training from scratch.
This approach improves the SOTA score on CoNLL04 dataset by 2.6% (micro) F1.
Paper: https://arxiv.org/abs/1909.07755
Code: https://github.com/markus-eberts/spert
#nlp #deeplearning #transformer #bert #ner #relationextraction
🦜 Hi!
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Channel was started as a collection of notable papers, news and releases shared for the members of Open Data Science (ODS) community. Through the years of just keeping the thing going we grew to an independent online Media supporting principles of Free and Open access to the information related to Data Science.
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We are the first Telegram Data Science channel.
Channel was started as a collection of notable papers, news and releases shared for the members of Open Data Science (ODS) community. Through the years of just keeping the thing going we grew to an independent online Media supporting principles of Free and Open access to the information related to Data Science.
Ultimate Posts
* Where to start learning more about Data Science. https://github.com/open-data-science/ultimate_posts/tree/master/where_to_start
* @opendatascience channel audience research. https://github.com/open-data-science/ods_channel_stats_eda
Open Data Science
ODS.ai is an international community of people anyhow related to Data Science.
Website: https://ods.ai
Hashtags
Through the years we accumulated a big collection of materials, most of them accompanied by hashtags.
#deeplearning #DL — post about deep neural networks (> 1 layer)
#cv — posts related to Computer Vision. Pictures and videos
#nlp #nlu — Natural Language Processing and Natural Language Understanding. Texts and sequences
#audiolearning #speechrecognition — related to audio information processing
#ar — augmeneted reality related content
#rl — Reinforcement Learning (agents, bots and neural networks capable of playing games)
#gan #generation #generatinveart #neuralart — about neural artt and image generation
#transformer #vqgan #vae #bert #clip #StyleGAN2 #Unet #resnet #keras #Pytorch #GPT3 #GPT2 — related to special architectures or frameworks
#coding #CS — content related to software engineering sphere
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Scaling Transformer to 1M tokens and beyond with RMT
Imagine extending the context length of BERT, one of the most effective Transformer-based models in natural language processing, to an unprecedented two million tokens! This technical report unveils the Recurrent Memory Transformer (RMT) architecture, which achieves this incredible feat while maintaining high memory retrieval accuracy.
The RMT approach enables storage and processing of both local and global information, allowing information flow between segments of the input sequence through recurrence. The experiments showcase the effectiveness of this groundbreaking method, with immense potential to enhance long-term dependency handling in natural language understanding and generation tasks, as well as enable large-scale context processing for memory-intensive applications.
Paper link: https://arxiv.org/abs/2304.11062
Code link: https://github.com/booydar/t5-experiments/tree/scaling-report
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-rmt-1m
#deeplearning #nlp #bert #memory
Imagine extending the context length of BERT, one of the most effective Transformer-based models in natural language processing, to an unprecedented two million tokens! This technical report unveils the Recurrent Memory Transformer (RMT) architecture, which achieves this incredible feat while maintaining high memory retrieval accuracy.
The RMT approach enables storage and processing of both local and global information, allowing information flow between segments of the input sequence through recurrence. The experiments showcase the effectiveness of this groundbreaking method, with immense potential to enhance long-term dependency handling in natural language understanding and generation tasks, as well as enable large-scale context processing for memory-intensive applications.
Paper link: https://arxiv.org/abs/2304.11062
Code link: https://github.com/booydar/t5-experiments/tree/scaling-report
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-rmt-1m
#deeplearning #nlp #bert #memory
