Long T5 Tglobal Base 16384 Booksum V12

🚀 pszemraj/long - t5 - tglobal - base - 16384 - booksum - V12

このモデルは、要約タスクに特化したTransformerベースのモデルです。長い文書やテキストの要約に強みを持ち、多くのデータセットで高いROUGEスコアを達成しています。

🚀 クイックスタート

このモデルは要約タスクに使用できます。以下にいくつかの使用例を示します。

地震関連のテキスト要約

原文: large earthquakes along a given fault segment do not occur at random intervals because it takes time to accumulate the strain energy for the rupture. The rates at which tectonic plates move and accumulate strain at their boundaries are approximately uniform. Therefore, in first approximation, one may expect that large ruptures of the same fault segment will occur at approximately constant time intervals. If subsequent main shocks have different amounts of slip across the fault, then the recurrence time may vary, and the basic idea of periodic mainshocks must be modified. For great plate boundary ruptures the length and slip often vary by a factor of 2. Along the southern segment of the San Andreas fault the recurrence interval is 145 years with variations of several decades. The smaller the standard deviation of the average recurrence interval, the more specific could be the long term prediction of a future mainshock.

要約結果: 特定の断層セグメントでの大地震はランダムな間隔で起こらず、応力エネルギーの蓄積に時間がかかるため、同じ断層セグメントの大きな破裂はおおよそ一定の間隔で起こると予想できます。ただし、すべり量によって再発時間は変化します。サンアンドレアス断層の南部セグメントでは再発間隔は145年で、数十年の変動があります。平均再発間隔の標準偏差が小さいほど、将来の主震の長期予測がより具体的になります。

科学論文のテキスト要約

原文: A typical feed - forward neural field algorithm. Spatiotemporal coordinates are fed into a neural network that predicts values in the reconstructed domain. Then, this domain is mapped to the sensor domain where sensor measurements are available as supervision. Class and Section Problems Addressed Generalization (Section 2) Inverse problems, ill - posed problems, editability; symmetries. Hybrid Representations (Section 3) Computation & memory efficiency, representation capacity, editability: Forward Maps (Section 4) Inverse problems Network Architecture (Section 5) Spectral bias, integration & derivatives. Manipulating Neural Fields (Section 6) Edit ability, constraints, regularization. Table 2: The five classes of techniques in the neural field toolbox each addresses problems that arise in learning, inference, and control. (Section 3). We can supervise reconstruction via differentiable forward maps that transform Or project our domain (e.g, 3D reconstruction via 2D images; Section 4) With appropriate network architecture choices, we can overcome neural network spectral biases (blurriness) and efficiently compute derivatives and integrals (Section 5). Finally, we can manipulate neural fields to add constraints and regularizations, and to achieve editable representations (Section 6). Collectively, these classes constitute a ''toolbox'' of techniques to help solve problems with neural fields There are three components in a conditional neural field: (1) An encoder or inference function € that outputs the conditioning latent variable 2 given an observation 0 E(0) = 2. 2 is typically a low - dimensional vector, and is often referred to aS a latent code Or feature code_ (2) A mapping function 4 between Z and neural field parameters O: Y(z) = O; (3) The neural field itself $. The encoder € finds the most probable z given the observations O: argmaxz P(2/0). The decoder maximizes the inverse conditional probability to find the most probable 0 given Z: arg - max P(Olz). We discuss different encoding schemes with different optimality guarantees (Section 2.1.1), both global and local conditioning (Section 2.1.2), and different mapping functions Y (Section 2.1.3) 2. Generalization Suppose we wish to estimate a plausible 3D surface shape given a partial or noisy point cloud. We need a suitable prior over the sur - face in its reconstruction domain to generalize to the partial observations. A neural network expresses a prior via the function space of its architecture and parameters 0, and generalization is influenced by the inductive bias of this function space (Section 5).

要約結果: 典型的なフィードフォワードニューラルフィールドアルゴリズムでは、時空間座標をニューラルネットワークに入力して再構成領域の値を予測します。ニューラルフィールドツールボックスには5種類の技術があり、学習、推論、制御で生じる問題を解決します。条件付きニューラルフィールドにはエンコーダ、マッピング関数、ニューラルフィールド自体の3つの要素があります。部分的またはノイズの多い点群から3D表面形状を推定するには、適切な事前分布が必要で、ニューラルネットワークの関数空間の帰納的バイアスが汎化に影響します。

講義の音声文字起こしのテキスト要約

原文: Is a else or outside the cob and tree written being of early client rope and you have is for good reasons. On to the ocean in Orange for time. By''s the aggregate we can bed it yet. Why this please pick up on a sort is do and also M Getoi''s nerocos and do rain become you to let so is his brother is made in use and Mjulia''s''s the lay major is aging Masastup coin present sea only of Oosii rooms set to you We do er do we easy this private oliiishs lonthen might be okay. Good afternoon everybody. Welcome to this lecture of Computational Statistics. As you can see, I''m not socially my name is Michael Zelinger. I''m one of the task for this class and you might have already seen me in the first lecture where I made a quick appearance. I''m also going to give the tortillas in the last third of this course. So to give you a little bit about me, I''m a old student here with better Bulman and my research centres on casual inference applied to biomedical disasters, so that could be genomics or that could be hospital data. If any of you is interested in writing a bachelor thesis, a semester paper may be mastathesis about this topic feel for reach out to me. you have my name on models and my email address you can find in the directory I''d Be very happy to talk about it. you do not need to be sure about it, we can just have a chat. So with that said, let''s get on with the lecture. There''s an exciting topic today I''m going to start by sharing some slides with you and later on during the lecture we''ll move to the paper. So bear with me for a few seconds. Well, the projector is starting up. Okay, so let''s get started. Today''s topic is a very important one. It''s about a technique which really forms one of the fundamentals of data science, machine learning, and any sort of modern statistics. It''s called cross validation. I know you really want to understand this topic I Want you to understand this and frankly, nobody''s gonna leave Professor Mineshousen''s class without understanding cross validation. So to set the stage for this, I Want to introduce you to the validation problem in computational statistics. So the problem is the following: You trained a model on available data. You fitted your model, but you know the training data you got could always have been different and some data from the environment. Maybe it''s a random process. You do not really know what it is, but you know that somebody else who gets a different batch of data from the same environment they would get slightly different training data and you do not care that your method performs as well. On this training data. you want to to perform well on other data that you have not seen other data from the same environment. So in other words, the validation problem is you want to quantify the performance of your model on data that you have not seen. So how is this even possible? How could you possibly measure the performance on data that you do not know The solution to? This is the following realization is that given that you have a bunch of data, you were in charge. You get to control how much that your model sees. It works in the following way: You can hide data firms model. Let''s say you have a training data set which is a bunch of doubtless so X eyes are the features those are typically hide and national vector. It''s got more than one dimension for sure. And the why why eyes. Those are the labels for supervised learning. As you''ve seen before, it''s the same set up as we have in regression. And so you have this training data and now you choose that you only use some of those data to fit your model. You''re not going to use everything, you only use some of it the other part you hide from your model. And then you can use this hidden data to do validation from the point of you of your model. This hidden data is complete by unseen. In other words, we solve our problem of validation.

要約結果: 講師のMichael Zelinger氏は、自身の研究内容や学生との相談について説明した後、今日の講義のテーマである交差検証について話し始めます。交差検証はデータサイエンスや機械学習の基礎的な技術で、モデルが未見のデータでどれだけ良い性能を発揮するかを評価するための手法です。訓練データの一部を隠して、その隠したデータを使ってモデルの検証を行うことで、未見のデータに対するモデルの性能を評価できます。

BigBirdモデルのブログ紹介のテキスト要約

原文: Transformer - based models have shown to be very useful for many NLP tasks. However, a major limitation of transformers - based models is its O(n^2)O(n 2) time & memory complexity (where nn is sequence length). Hence, it''s computationally very expensive to apply transformer - based models on long sequences n > 512n>512. Several recent papers, e.g. Longformer, Performer, Reformer, Clustered attention try to remedy this problem by approximating the full attention matrix. You can checkout 🤗''s recent blog post in case you are unfamiliar with these models. BigBird (introduced in paper) is one of such recent models to address this issue. BigBird relies on block sparse attention instead of normal attention (i.e. BERT''s attention) and can handle sequences up to a length of 4096 at a much lower computational cost compared to BERT. It has achieved SOTA on various tasks involving very long sequences such as long documents summarization, question - answering with long contexts. BigBird RoBERTa - like model is now available in 🤗Transformers. The goal of this post is to give the reader an in - depth understanding of big bird implementation & ease one''s life in using BigBird with 🤗Transformers. But, before going into more depth, it is important to remember that the BigBird''s attention is an approximation of BERT''s full attention and therefore does not strive to be better than BERT''s full attention, but rather to be more efficient. It simply allows to apply transformer - based models to much longer sequences since BERT''s quadratic memory requirement quickly becomes unbearable. Simply put, if we would have ∞ compute & ∞ time, BERT''s attention would be preferred over block sparse attention (which we are going to discuss in this post). If you wonder why we need more compute when working with longer sequences, this blog post is just right for you! Some of the main questions one might have when working with standard BERT - like attention include: Do all tokens really have to attend to all other tokens? Why not compute attention only over important tokens? How to decide what tokens are important? How to attend to just a few tokens in a very efficient way? In this blog post, we will try to answer those questions. What tokens should be attended to? We will give a practical example of how attention works by considering the sentence ''BigBird is now available in HuggingFace for extractive question answering''. In BERT - like attention, every word would simply attend to all other tokens. Let''s think about a sensible choice of key tokens that a queried token actually only should attend to by writing some pseudo - code. Will will assume that the token available is queried and build a sensible list of key tokens to attend to. >>> # let''s consider following sentence as an example >>> example = [''BigBird'', ''is'', ''now'', ''available'', ''in'', ''HuggingFace'', ''for'', ''extractive'', ''question'', ''answering''] >>> # further let''s assume, we''re trying to understand the representation of ''available'' i.e. >>> query_token = ''available'' >>> # We will initialize an empty `set` and fill up the tokens of our interest as we proceed in this section. >>> key_tokens = [] # => currently ''available'' token doesn''t have anything to attend Nearby tokens should be important because, in a sentence (sequence of words), the current word is highly dependent on neighboring past & future tokens. This intuition is the idea behind the concept of sliding attention.

要約結果: Transformerベースのモデルは多くのNLPタスクで有用ですが、O(n^2)の時間とメモリ複雑度が問題で、長いシーケンスに適用するのは計算コストが高いです。BigBirdはこの問題を解決するためにブロック疎注意力を使用し、BERTよりも低い計算コストで最大4096のシーケンスを処理できます。BigBirdは長文書要約や長文脈の質問応答などのタスクでSOTAを達成しています。このブログでは、BigBirdの実装について詳しく説明し、標準的なBERTの注意力に関するいくつかの質問に答えます。また、どのトークンに注意力を向けるべきかについて、具体的な例を使って説明します。

Rick and Mortyに関するテキスト要約

原文: To be fair, you have to have a very high IQ to understand Rick and Morty. The humour is extremely subtle, and without a solid grasp of theoretical physics most of the jokes will go over a typical viewer''s head. There''s also Rick''s nihilistic outlook, which is deftly woven into his characterisation - his personal philosophy draws heavily from Narodnaya Volya literature, for instance. The fans understand this stuff; they have the intellectual capacity to truly appreciate the depths of these jokes, to realise that they''re not just funny - they say something deep about LIFE. As a consequence people who dislike Rick & Morty truly ARE idiots - of course they wouldn''t appreciate, for instance, the humour in Rick''s existential catchphrase ''Wubba Lubba Dub Dub,'' which itself is a cryptic reference to Turgenev''s Russian epic Fathers and Sons. I''m smirking right now just imagining one of those addlepated simpletons scratching their heads in confusion as Dan Harmon''s genius wit unfolds itself on their television screens. What fools.. how I pity them. 😂 And yes, by the way, i DO have a Rick & Morty tattoo. And no, you cannot see it. It''s for the ladies'' eyes only - and even then they have to demonstrate that they''re within 5 IQ points of my own (preferably lower) beforehand. Nothin personnel kid 😎

要約結果: Rick and Mortyを理解するには高いIQが必要で、そのユーモアは非常に繊細で、理論物理学を理解していないと多くのジョークが理解できません。Rickのニヒリスト的な世界観も彼のキャラクターに巧みに織り込まれています。この作品を嫌う人は本当に馬鹿だと言われています。作者はRick and Mortyの入れ墨を持っており、女性のみに見せるという条件を設定しています。

✨ 主な機能

• 長文書要約: 長い文書やテキストを効率的に要約できます。
• 低コスト: BigBirdのような手法を用いることで、計算コストを抑えつつ長いシーケンスを処理できます。
• 高精度: 多くのデータセットで高いROUGEスコアを達成しています。

📦 インストール

このモデルは🤗Transformersライブラリを通じて使用できます。以下のコマンドでライブラリをインストールできます。

pip install transformers

📚 ドキュメント

パラメータ設定

評価指標

このモデルはROUGEスコアを用いて評価されています。具体的な結果は以下の通りです。

🔧 技術詳細

このモデルはTransformerベースのモデルで、BigBirdのような手法を用いて長いシーケンスを処理します。BigBirdはブロック疎注意力を使用することで、計算コストを抑えつつ長いシーケンスを扱うことができます。

📄 ライセンス

このモデルは以下のライセンスの下で公開されています。

• Apache - 2.0
• BSD - 3 - Clause