Apr 4, 2023 · Transformer-XL is a transformer-based language model with a segment-level recurrence and a novel relative positional encoding. Enhancements introduced in Transformer-XL help capture better long-term dependencies by attending to tokens from multiple previous segments. Our implementation is based on the codebase published by the authors of the ... in the streaming fashion, we introduce the Transformer-XL [3] based steaming model, which is computationally tractable for inference. Our results show that Transformer-XL is on par with latency-controlled BLSTM (LC-BLSTM) [15] with the same latency constraint. 2. Related Work There have been a few studies on Transformers for end-to-end Model Details. Model Description: GPT-2 XL is the 1.5B parameter version of GPT-2, a transformer-based language model created and released by OpenAI. The model is a pretrained model on English language using a causal language modeling (CLM) objective. Developed by: OpenAI, see associated research paper and GitHub repo for model developers.Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a novel positional encoding scheme. Our method ...in the streaming fashion, we introduce the Transformer-XL [3] based steaming model, which is computationally tractable for inference. Our results show that Transformer-XL is on par with latency-controlled BLSTM (LC-BLSTM) [15] with the same latency constraint. 2. Related Work There have been a few studies on Transformers for end-to-end Jan 29, 2019 · Empirically, Transformer-XL enjoys three benefits: Transformer-XL learns dependency that is about 80% longer than RNNs and 450% longer than vanilla Transformers, which generally have better performance than RNNs, but are not the best for long-range dependency modeling due to fixed-length contexts (please see our paper for details). Oct 11, 2020. 1. This paper (“Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context”) was published in ACL 2019, one of the top NLP conferences, by researchers at Google AI. It proposes Transformer-XL, a new architecture that enables natural language understanding beyond a fixed-length context without disrupting temporal ...The Transformer-XL model addresses the limitations of vanilla transformer-based language models, which are only able to use relatively short context, bounded by the segment length. The Transformer-XL introduces a recurrence mechanism, which is able to use a cached hidden state from previous segments. Transformer-XL (meaning extra long) is a Transformer architecture that introduces the notion of recurrence to the deep self-attention network. Instead of computing the hidden states from scratch for each new segment, Transformer-XL reuses the hidden states obtained in previous segments. A new paper by Google and Carnegie Mellon University, “ Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context”, combines these two approaches. The new model uses the Transformer’s attention modules on each segment of input data and a recurrence mechanism to learn dependencies between consecutive segments.Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism ... Write With Transformer is a webapp created and hosted by Hugging Face showcasing the generative capabilities of several models. GPT-2 is one of them and is available in five different sizes: small, medium, large, xl and a distilled version of the small checkpoint: distilgpt-2. This model was contributed by thomwolf. Apr 1, 2020 · 이번 글에서는 ACL 2019에서 발표된 “Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context”를 리뷰하려고 합니다. 본 논문은 기존의 Transformer 구조를 이용한 고정된 길이(Fixed-Length) Language Model의 한계점을 지적하고 더 긴 의존성을 이용할 수 있는 새로운 방법을 제시합니다. The Transformer XL is a new approach to deep learning models that are designed to handle long-sequence modeling tasks. It is an extension of the Transformer architecture that was first introduced ...Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism ...Model Details. Model Description: GPT-2 XL is the 1.5B parameter version of GPT-2, a transformer-based language model created and released by OpenAI. The model is a pretrained model on English language using a causal language modeling (CLM) objective. Developed by: OpenAI, see associated research paper and GitHub repo for model developers. Transformer Architecture. XLNET integrates ideas from Transformer-XL, the state-of-the-art autoregressive model into pretraining. Transformer is a model used for language translation purposes by google. It basically revolves around “attention”. It is an encoder-decoder model where you map one sequence to another — English to French.Transformers. Transformers are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings. in the streaming fashion, we introduce the Transformer-XL [3] based steaming model, which is computationally tractable for inference. Our results show that Transformer-XL is on par with latency-controlled BLSTM (LC-BLSTM) [15] with the same latency constraint. 2. Related Work There have been a few studies on Transformers for end-to-endTransformer-XL achieved SOTA results following datasets - WikiText-103, enwik8, text8, One Billion Word and Penn Treebank. Transformer-XL has also been used to generate text. Examples are given at ...Jun 15, 2020 · Transformers Xl was released about a year ago and the main motive behind it was to improve more over vanilla transformers. Transformers XL was made to address the problem of context fragmentation. Jul 26, 2019 · Transformer-XL achieved SOTA results following datasets - WikiText-103, enwik8, text8, One Billion Word and Penn Treebank. Transformer-XL has also been used to generate text. Examples are given at ... The Transformer-XL model was proposed in Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context by Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov. It’s a causal (uni-directional) transformer with relative positioning (sinusoïdal) embeddings which can reuse previously computed hidden ...Jan 11, 2019 · Transformer-XL obtains strong results for both word-level and character-level language modeling applied to a variety of datasets such as WikiText-103, text8, and One Billion Word. Comparison of the model architecture of Transformer-XL, Transformer-XL with the layer norm reordered, and Gated Transformer-XL. (Image source: Figure 1 in Parisotto, et al. 2019 ) Decision Transformer ( DT ; Chen et al 2021 ) formulates Reinforcement Learning problems as a process of conditional sequence modeling , outputting the optimal ...PyTorch-Transformers (formerly known as pytorch-pretrained-bert) is a library of state-of-the-art pre-trained models for Natural Language Processing (NLP). The library currently contains PyTorch implementations, pre-trained model weights, usage scripts and conversion utilities for the following models: BERT (from Google) released with the paper ...Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural ar-chitecture Transformer-XL that enables learn-ing dependency beyond a fixed length with-out disrupting temporal coherence. It con-sists of a segment-level recurrence mechanism Jan 1, 2019 · Various methods have been proposed to introduce memorization capabilities to Transformers through recurrence [5,38]. Transformer-XL [8] feeds the input to the model in windows of a fixed length ... Apr 4, 2023 · Transformer-XL is a transformer-based language model with a segment-level recurrence and a novel relative positional encoding. Enhancements introduced in Transformer-XL help capture better long-term dependencies by attending to tokens from multiple previous segments. Our implementation is based on the codebase published by the authors of the ... Feb 25, 2021 · As a side note, we remark that this conclusion is reached based on the assumption that key and query sizes are the same. It may be possible in a context like Transformer-XL, that there is global positional or contextual information that could be propagated in the network. In this case it might not be prudent to discard these contributions. We propose architectural modifications that substantially improve the stability and learning speed of the original Transformer and XL variant. The proposed architecture, the Gated Transformer-XL (GTrXL), surpasses LSTMs on challenging memory environments and achieves state-of-the-art results on the multi-task DMLab-30 benchmark suite, exceeding ...Transformer-XL is one of the few models that has no sequence length limit. Same as a regular GPT model, but introduces a recurrence mechanism for two consecutive segments (similar to a regular RNNs with two consecutive inputs). Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism ...{"payload":{"allShortcutsEnabled":false,"fileTree":{"pytorch":{"items":[{"name":"utils","path":"pytorch/utils","contentType":"directory"},{"name":".DS_Store","path ...Dec 5, 2022 · Chinese-Transformer-XL. Under construction. 本项目提供了智源研究院"文汇" 预训练模型Chinese-Transformer-XL的预训练和文本生成代码。 The Transformer-XL model was proposed in Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context by Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov. It’s a causal (uni-directional) transformer with relative positioning (sinusoïdal) embeddings which can reuse previously computed hidden ...Model architecture. The model is built from the transformer-XL [ 7] architecture. In general, transformer models are increasingly replacing recurrent neural networks, as these architectures have shown to be better suited for optimization on sequential data, resulting in improved training times and performances.from Transformer-XL, the state-of-the-art autoregressive model, into pretraining. Empirically, under comparable experiment setting, XLNet outperforms BERT on 20 tasks, often by a large margin, including question answering, natural language inference, sentiment analysis, and document ranking.1. 1 Introduction教你怎样用Transformer-XL及其进化XLNet. 最近又重新读了Transformer-XL和XLNet的论文和代码，又有很多新的感悟。. 其中，要想搞懂XLNet的同学一定要首先明白Transofrmer-XL，因为XLNet是基于Transformer-XL进行改进的。. tips：Transformer-XL投稿是被ICLR 2019拒稿的，作者基于Transformer ...The net result: a 64-GPU version of small Transformer-XL model trains about 44x faster than the original “slow” 4-GPU implementation. Our Transformer-XL with 75M parameters (equivalent to 186M in the paper) trains 13.2x faster on 128 GPUs than on 8 GPUs. The training procedure required changes to prevent numerical divergence at larger batch ...The Transformer-XL model was proposed in Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context by Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov. It’s a causal (uni-directional) transformer with relative positioning (sinusoïdal) embeddings which can reuse previously computed hidden ... Transformer. A transformer model. User is able to modify the attributes as needed. The architecture is based on the paper “Attention Is All You Need”. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Abstract. Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence ...The Gated Transformer-XL (GTrXL; Parisotto, et al. 2019) is one attempt to use Transformer for RL. GTrXL succeeded in stabilizing training with two changes on top of Transformer-XL : The layer normalization is only applied on the input stream in a residual module, but NOT on the shortcut stream.Transformer-XL was able to learn dependency 80% longer than RNNs and 450% longer than Vanilla Transformer. You heard it right, a whooping 450%! Transformer-XL is also a mind-blowing 1800 times faster than Vanilla Transformers. These numbers are very huge claims. Let’s dig deep into the architecture and understand the mechanism by which it is ...transformers; it caches the (key,value) pairs computed from the previous training step, and uses them as a prefix for the tokens on the next training step, which yields significant gains on long documents. Rae et al. (2020) improve over Transformer-XL by compressing the tokens before adding them to the 2Comparison of the model architecture of Transformer-XL, Transformer-XL with the layer norm reordered, and Gated Transformer-XL. (Image source: Figure 1 in Parisotto, et al. 2019 ) Decision Transformer ( DT ; Chen et al 2021 ) formulates Reinforcement Learning problems as a process of conditional sequence modeling , outputting the optimal ...Jan 11, 2019 · Transformer-XL obtains strong results for both word-level and character-level language modeling applied to a variety of datasets such as WikiText-103, text8, and One Billion Word. Transformer-XL learns dependencies that are approximately 80% longer than RNNs and 450% longer than vanilla Transformers, which generally have better performance than RNNs, but are not the best ...The Transformer-XL model was proposed in Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov. It’s a causal (uni-directional) transformer with relative positioning (sinusoïdal) embeddings which can reuse previously computed hidden ... in the streaming fashion, we introduce the Transformer-XL [3] based steaming model, which is computationally tractable for inference. Our results show that Transformer-XL is on par with latency-controlled BLSTM (LC-BLSTM) [15] with the same latency constraint. 2. Related Work There have been a few studies on Transformers for end-to-end 3. Results: TransformerXL đạt được kết quả SOTA ( State of The Art ) trên nhiều datasets benchmarks về Language Modeling trên cả mức word-level và character-level. Trên WikiText-103, một bộ dataset lớn về Language Modeling ở mức word-level, TransformerXL (18 layers) đạt perplexity bằng 18.3 so với ...Jul 18, 2019 · Transformer-XL. Transformer networks are limited by a fixed-length context and thus can be improved through learning longer-term dependency. That’s why Google proposed a novel method called Transformer-XL (meaning extra long) for language modeling, which enables a Transformer architecture to learn longer-term dependency. Transformer-XL is up ... Transformer-XL is an autoregressive model (not bi-directional like BERT). It has 2 main advantages over its competitors: Transformer-XL can learn longer context. The authors claim that it can learn dependency that is 450% longer than vanilla Transformer, thanks to the ability to handle the problem of context segmentation. Transformer XL. This is an experiment training Shakespeare dataset with a Transformer XL model. Unlike the vanilla Transformer [7], MHA uses relative positional encodings from Transformer-XL [26]. The key component of Conformer is the Conv module which contains a pointwise convolution ...The structure of the GTrXL (Gated Transformer XL) block is illustrated in detail below: The architecture used for text generation is the one proposed in the paper Stabilizing Transformers for Reinforcement Learning. Music generation requires a modified model where the input features are split into MIDI events (note_on, note_off and control ...3. Results: TransformerXL đạt được kết quả SOTA ( State of The Art ) trên nhiều datasets benchmarks về Language Modeling trên cả mức word-level và character-level. Trên WikiText-103, một bộ dataset lớn về Language Modeling ở mức word-level, TransformerXL (18 layers) đạt perplexity bằng 18.3 so với ...this setting, Transformer-XL learns a RECL of 900 words on W ikiT ext-103, while the numbers for. recurrent networks and Transformer are only 500 and 128. 2 R E L ATE D W ORK.Existing Approaches for Long Document Transformers via Longformer Paper. The paper initially addresses the issues with existing long document transformers. Models like Transformer-XL partitions the input and apply full self-attention locally as well as in a cross-partition setting (to an extent).Aug 25, 2023 · Transformer-XL is a neural network model that can handle long sequences of text or speech with high efficiency and accuracy. It is based on the Transformer architecture, but with some key ... This is the OG transformer that started the revolution. TransformerXL —this forward-directional decoder is an amazing text generator. Memory and relative positional encoding enable super fast and accurate predictions. We used this model in Part II.We've installed transformer-xl onto our server and are writing a keras script for building, finetuning and testing our transformer-xl model. 4/2/20: Overview: Amongst other goals, scripts are being developed to significantly speed-up the testing and comparing process, to hopefully increase development efficiency. Edward:A new paper by Google and Carnegie Mellon University, “ Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context”, combines these two approaches. The new model uses the Transformer’s attention modules on each segment of input data and a recurrence mechanism to learn dependencies between consecutive segments.In particular, Transformer-XL backbone and the permutation LM play a heavy role in improving XLNet’s performance over that of BERT. RACE (ReAding Comprehension from Examinations) dataset is a ...transformer xl在中文文本生成上的尝试（可写小说、古诗）（transformer xl for text generation of chinese） - GitHub - GaoPeng97/transformer-xl ...This implements the Retrieval-Enhanced Transformer (RETRO). Compressive Transformer. This is an implementation of compressive transformer that extends upon Transformer XL by compressing the oldest memories to give a longer attention span. GPT Architecture. This is an implementation of GPT-2 architecture. GLU VariantsThe Transformer XL is a new approach to deep learning models that are designed to handle long-sequence modeling tasks. It is an extension of the Transformer architecture that was first introduced ...Transformer XL is an important variation of Transformers as it improves upon a major shortcoming of transformers, context fragmentation. It improved the speed of training and allowed the model to capture longer dependencies. Improvements upon this transformer like the XLNet are beating BERT at critical language tasks.Transformer-XL achieves new state-of-the-art results on multiple language modeling benchmarks. Transformer-XL is also the first to break through the 1.0 barrier on char-level language modeling. Below is a summary.As a side note, we remark that this conclusion is reached based on the assumption that key and query sizes are the same. It may be possible in a context like Transformer-XL, that there is global positional or contextual information that could be propagated in the network. In this case it might not be prudent to discard these contributions.Jul 18, 2019 · Transformer-XL. Transformer networks are limited by a fixed-length context and thus can be improved through learning longer-term dependency. That’s why Google proposed a novel method called Transformer-XL (meaning extra long) for language modeling, which enables a Transformer architecture to learn longer-term dependency. Transformer-XL is up ... Transformer-XL is a neural network model that can handle long sequences of text or speech with high efficiency and accuracy. It is based on the Transformer architecture, but with some key ...Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a novel positional encoding scheme. Our method ...Oct 13, 2019 · We propose architectural modifications that substantially improve the stability and learning speed of the original Transformer and XL variant. The proposed architecture, the Gated Transformer-XL (GTrXL), surpasses LSTMs on challenging memory environments and achieves state-of-the-art results on the multi-task DMLab-30 benchmark suite, exceeding ... Jan 1, 2019 · Various methods have been proposed to introduce memorization capabilities to Transformers through recurrence [5,38]. Transformer-XL [8] feeds the input to the model in windows of a fixed length ... Transformer-XL is an autoregressive model (not bi-directional like BERT). It has 2 main advantages over its competitors: Transformer-XL can learn longer context. The authors claim that it can learn dependency that is 450% longer than vanilla Transformer, thanks to the ability to handle the problem of context segmentation.See full list on towardsdatascience.com Transformers Xl was released about a year ago and the main motive behind it was to improve more over vanilla transformers. Transformers XL was made to address the problem of context fragmentation.this setting, Transformer-XL learns a RECL of 900 words on W ikiT ext-103, while the numbers for. recurrent networks and Transformer are only 500 and 128. 2 R E L ATE D W ORK.This is the standard input to Transformer XL and is commonly referred to as h in XLNet. relative_position_encoding: Relative positional encoding Tensor of shape [B, L, dim]. segment_matrix: Optional Tensor of shape [B, S, S + M]. Used in XLNet, but not in Transformer XL. segment_embedding: Optional Tensor of shape [2, num_heads, dim]. Used in ...Apr 4, 2023 · Transformer-XL is a transformer-based language model with a segment-level recurrence and a novel relative positional encoding. Enhancements introduced in Transformer-XL help capture better long-term dependencies by attending to tokens from multiple previous segments. Our implementation is based on the codebase published by the authors of the ...
Transformer-XL is one of the few models that has no sequence length limit. Same as a regular GPT model, but introduces a recurrence mechanism for two consecutive segments (similar to a regular RNNs with two consecutive inputs). . Uta men
Oct 13, 2019 · We propose architectural modifications that substantially improve the stability and learning speed of the original Transformer and XL variant. The proposed architecture, the Gated Transformer-XL (GTrXL), surpasses LSTMs on challenging memory environments and achieves state-of-the-art results on the multi-task DMLab-30 benchmark suite, exceeding ... Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism ...Hi, you will likely need to adapt this example since Transformer-XL uses memory cells but there is no ready to use example for fine-tuning Transformer-XL in the repo unfortunately (and I don't plan to add one in the near future). If you want to give it a try feel free to ask more specific questions here.Aug 25, 2023 · Transformer-XL is a neural network model that can handle long sequences of text or speech with high efficiency and accuracy. It is based on the Transformer architecture, but with some key ... {"payload":{"allShortcutsEnabled":false,"fileTree":{"pytorch":{"items":[{"name":"utils","path":"pytorch/utils","contentType":"directory"},{"name":".DS_Store","path ...Apr 4, 2023 · Transformer-XL is a transformer-based language model with a segment-level recurrence and a novel relative positional encoding. Enhancements introduced in Transformer-XL help capture better long-term dependencies by attending to tokens from multiple previous segments. Our implementation is based on the codebase published by the authors of the ... Jul 8, 2020 · Transformer-XL. The Transformer-XL model is based on a similar idea as the vanilla model, but with some corrections. In the following subsections we’ll be discussing the contributions of the Transformer-XL architecture and see how it was able to achieve the state of the art. XL stands for eXtra Long. Segment Recurrence Mechanism Mar 13, 2021 · Transformer XL is an important variation of Transformers as it improves upon a major shortcoming of transformers, context fragmentation. It improved the speed of training and allowed the model to capture longer dependencies. Improvements upon this transformer like the XLNet are beating BERT at critical language tasks. Transformers. Transformers are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings. Comparison of the model architecture of Transformer-XL, Transformer-XL with the layer norm reordered, and Gated Transformer-XL. (Image source: Figure 1 in Parisotto, et al. 2019 ) Decision Transformer ( DT ; Chen et al 2021 ) formulates Reinforcement Learning problems as a process of conditional sequence modeling , outputting the optimal ...The Transformer-XL model was proposed in Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov. It’s a causal (uni-directional) transformer with relative positioning (sinusoïdal) embeddings which can reuse previously computed hidden ...May 19, 2021 · The combination of Transformer architecture and transfer learning is dominating the Natural Language Processing world. There are numerous pre-trained models (Huggingface alone has 40+) which might ... Apr 7, 2020 · The Gated Transformer-XL (GTrXL; Parisotto, et al. 2019) is one attempt to use Transformer for RL. GTrXL succeeded in stabilizing training with two changes on top of Transformer-XL : The layer normalization is only applied on the input stream in a residual module, but NOT on the shortcut stream. The Gated Transformer-XL (GTrXL; Parisotto, et al. 2019) is one attempt to use Transformer for RL. GTrXL succeeded in stabilizing training with two changes on top of Transformer-XL : The layer normalization is only applied on the input stream in a residual module, but NOT on the shortcut stream.Transformer-XL 在 vanilla Transformer 模型基础上改进，通过引入循环机制和注意力机制，允许模型学习长期依赖性， 有以下几点优势：. 1. 解决长距离依赖问题. 2. 解决segment间语义不完整问题. 3. 解决计算慢的问题. 按照论文的描述，TransformerXL学习的依赖关系比RNN长80% ...{"payload":{"allShortcutsEnabled":false,"fileTree":{"examples/pytorch/text-generation":{"items":[{"name":"README.md","path":"examples/pytorch/text-generation/README ...Aug 25, 2023 · Transformer-XL is a neural network model that can handle long sequences of text or speech with high efficiency and accuracy. It is based on the Transformer architecture, but with some key ... In particular, Transformer-XL backbone and the permutation LM play a heavy role in improving XLNet’s performance over that of BERT. RACE (ReAding Comprehension from Examinations) dataset is a ...This is the OG transformer that started the revolution. TransformerXL —this forward-directional decoder is an amazing text generator. Memory and relative positional encoding enable super fast and accurate predictions. We used this model in Part II..