| --- |
| license: |
| - apache-2.0 |
| - bsd-3-clause |
| tags: |
| - summarization |
| - summary |
| - booksum |
| - long-document |
| - long-form |
| datasets: |
| - kmfoda/booksum |
| metrics: |
| - rouge |
| widget: |
| - text: 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. |
| example_title: earthquakes |
| - text: ' 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).' |
| example_title: scientific paper |
| - text: '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.' |
| example_title: transcribed audio - lecture |
| - text: '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.' |
| example_title: bigbird blog intro |
| - text: '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 😎' |
| example_title: Richard & Mortimer |
| parameters: |
| max_length: 64 |
| min_length: 8 |
| no_repeat_ngram_size: 3 |
| early_stopping: true |
| repetition_penalty: 3.5 |
| length_penalty: 0.3 |
| encoder_no_repeat_ngram_size: 3 |
| num_beams: 4 |
| model-index: |
| - name: pszemraj/long-t5-tglobal-base-16384-booksum-V12 |
| results: |
| - task: |
| type: summarization |
| name: Summarization |
| dataset: |
| name: samsum |
| type: samsum |
| config: samsum |
| split: test |
| metrics: |
| - type: rouge |
| value: 30.0032 |
| name: ROUGE-1 |
| verified: true |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjk2MTRiNDljZTM4NzliNDdmMTdkZGY3MGY4OTVmMzFhOTdjNGFjYjJhYTBjYTI4Y2VkOGMxYWI5M2M3YWEyZSIsInZlcnNpb24iOjF9.cZtcCwB1Bnnn1g4x8Ia_8oTSK89feGF80r20jwjSb-xy5Xt3eR3dOVjJyjurfN0UOGyEe7inTpneJhcAoRwwBg |
| - type: rouge |
| value: 7.2671 |
| name: ROUGE-2 |
| verified: true |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNThiYmJhN2NkYmU0MmZmZGY5MGU2NmEzZGQwNjM0MDEwNzlhNDgzY2E2MzkxMWVkZTUwMWFlZmFhYWEwN2M5ZSIsInZlcnNpb24iOjF9.IaaaHiOxUdh6IDGbb2vCCEcL-YhXCtaFlZnIpcgQwsC3KRgfrpQi5vdhyaaIJSieA2pzbFjUO--WqjylvpysCA |
| - type: rouge |
| value: 21.8779 |
| name: ROUGE-L |
| verified: true |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTc1N2YwODk4YmU1Mjk3NGQ2ZDVkYWVjN2Y1ZDVlOTNkMjU5MjcyYjY0ZWY5NjJkNzZjNjMwZWUxNWY0NTY1ZiIsInZlcnNpb24iOjF9.HhYA0t2Ee3YhtBDPneU7hzEEz5c4FeBcTo-3TSSClltG3A5E3RIgbxUbQNbldRAL9Y44Z8uzEHfe676eL22vBg |
| - type: rouge |
| value: 26.4371 |
| name: ROUGE-LSUM |
| verified: true |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTJmZmJhZTBiZDczYmNkNWQ0MGQ3ZTIyNzc2NGExMGY1MGNkOThlNDg0OWQ3YWFmNDRmYTUxZTYzN2U5Yzc4MCIsInZlcnNpb24iOjF9.fgr8NNlhDCvtXMudOce1pf_slujIhXAEC3a6fH6AAlgIvzxg1oGV5QiUcrPDNhyFD2XazZ39Xk1GhoMk4AnxAQ |
| - type: loss |
| value: 2.6383285522460938 |
| name: loss |
| verified: true |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjRiMjAyMjJkM2M5NGZjYzRiZGFlNTJhM2UyNjExODlmNjM4NjRmZTRlMWEzMTUzYTI2NjYzYTAyNmVlYjJjMCIsInZlcnNpb24iOjF9.wKAqpXyvHNGDpxwLmR6mzI4gRwVQI88uFJZJoRAWQD_d-H97y5cpP4VSBes_YfVpFpYzEF8miN9fv660xukiBA |
| - type: gen_len |
| value: 54.2357 |
| name: gen_len |
| verified: true |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzA1Y2IxN2Q4OGU0N2FkNDFmNTFmMjQwZDA4MTczMDJmNWIyMjdhYzhkNTE5ZjI4M2NjZTdkMmUwMTFjMzk1ZCIsInZlcnNpb24iOjF9.JuADjJNIcaqmZTw1RFnklHJYEYfTEKQ0YnmvL1TmvSihIVJORbK-3cFkJLVJdyaaRq40HjhQRw6mmpur9Lq1CQ |
| - task: |
| type: summarization |
| name: Summarization |
| dataset: |
| name: launch/gov_report |
| type: launch/gov_report |
| config: plain_text |
| split: test |
| metrics: |
| - type: rouge |
| value: 37.0538 |
| name: ROUGE-1 |
| verified: true |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzViY2Y2ZWIwMDdhNDEzMDU3MmE4ZTBlZjQ2MDI2YTVjOGZjZDM5NzhiZDk2MWJhZWY5MDUwY2NhZTY2OTc5ZSIsInZlcnNpb24iOjF9.p2z_oZD9uVTnBtf7vRRKvisW-rXWVibpU0QQ-S_16CIYLc2kTJRZMLzaMJqbi1d8icBTeG5PdIzKcAVwu7JKCA |
| - type: rouge |
| value: 8.1512 |
| name: ROUGE-2 |
| verified: true |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWUzZGM0ZGJiMDYwM2ZmYjI5Mzk5MTU2N2JlZGVlOGRjMTJjY2QwOWIwMjgyMjM0ZjIzY2Q4MzJjNDkxZmVhMCIsInZlcnNpb24iOjF9.z6pMF8l4uMQIEcdyU1kgDc1v3rCn-0TVxntKP3hmOEwRJqfbeqDmhhAROWadYTPNewpfsCpShVHGJt9DvH55BQ |
| - type: rouge |
| value: 17.6645 |
| name: ROUGE-L |
| verified: true |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZWNkYzY2NGY4YmFiNWRhODAwZmFmOTkzM2M3MGY0ZTQzZTUwNmExNDc5ZDdhZWVhZjFhYTUyYjFlZjQ3ZDA4ZCIsInZlcnNpb24iOjF9.XbVCDhR_l7OalwF2DsHJSZ39z_HHdG3PlwKL0Ls9lBvRo4E8sk00vrQy4IRCqPF8hPJusl2Nb65V3CvgIldqAA |
| - type: rouge |
| value: 33.4275 |
| name: ROUGE-LSUM |
| verified: true |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDdiYzI0MDlmYjg0MWFjZDBmMmIyZWUyNzNhYTUyNTU1ZDdhODE4ZTlmMTg5MDY1MDhhMGRlMGU1OTA3YzM4ZSIsInZlcnNpb24iOjF9.pDHKUDMXHihmLSQzYq6bxclcLyajcRf6Q5ImhpvpoepG8du5ggwb1q_2anGfDjJ0kkFa-Iwtbl8KmdqD7TTCAQ |
| - type: loss |
| value: 2.6052205562591553 |
| name: loss |
| verified: true |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjk0YWNjMjkxZjUwMDBlODNkNjE0ZWRkYzYxZmRjNjBhMmVjNTE2OWFkZTU1OTYzMzMxNzdkMGFlODVjOWVkNCIsInZlcnNpb24iOjF9.n-p8JJBe9nOsKwvS2CHO6HBiI6b-0dUZuVaL9aQgX_qFhETvwR_gHggWXU6sCiLCzkElH6ZpGpcMw9AogJWkCw |
| - type: gen_len |
| value: 201.5951 |
| name: gen_len |
| verified: true |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzMyYWViNDNjMzY2NmQyZjI5MWU2ZjMwMmYyOGFkMzM0YzgwMzg5ZDhmYzYzYzg0OTMzOWY5ZDRiM2NkNWViOSIsInZlcnNpb24iOjF9.6T6C1dimUVOHNbqm5drVZmiWVrQEC0VBc7nSAiyLm2K3WE99FisSByk4zhBtUf_CntT_TZm1dBpfTaAUVPDOAQ |
| - task: |
| type: summarization |
| name: Summarization |
| dataset: |
| name: kmfoda/booksum |
| type: kmfoda/booksum |
| config: kmfoda--booksum |
| split: test |
| metrics: |
| - type: rouge |
| value: 36.1423 |
| name: ROUGE-1 |
| verified: true |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTZkYTA5N2FhNjVhMzg1ZDRjOThhZjcwMjdmYzQ1MGE5N2RhNTM0MmNjMzVkYjNlYmZjOGZjMDFlZDBkMGM5MSIsInZlcnNpb24iOjF9.odQ-NMcQ06o2mqzXOfGY1c967_RUfg93YfGnMTpKUXPM5dGawkdVYGO8rPCHt5bttPvYlBmRgNl6Z7H_OhgnCA |
| - type: rouge |
| value: 5.634 |
| name: ROUGE-2 |
| verified: true |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZmFkODViOTg2MDYxZDhlMjZiOTNjZWE2ZTI5YmVhYWRiNGM1OTAzZDEzN2Y1ODI4OWI3NzU2ZmZlMGJjNGIyZiIsInZlcnNpb24iOjF9.4-VpnxVDiC0AG-de1dFr6VHNNbK2qZhAMQ62EpVU7Et-n25w8GPcoyr9l4AXIodQpU6p0H0pdntEUqQwJOHaDg |
| - type: rouge |
| value: 16.3747 |
| name: ROUGE-L |
| verified: true |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzkzYWY1NmEyMWNkODQ2N2ExYzMwNWExZDgwNTkxMTg5OTNjYjU5NjMwNWU3NzZhZDYwYzA4M2I0ZmU3Yjg2NiIsInZlcnNpb24iOjF9.tY2mQ0bZU9GMYYTJPot_vgvmiAoubdYWAzEQSQskigleh7AWtsXbO2CnhBsE_7UpsLPVWGccP0IWkHdHRg9zAA |
| - type: rouge |
| value: 33.0665 |
| name: ROUGE-LSUM |
| verified: true |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTEyZGZlNmRhNjllMGExZTJhOWE0NDQwN2Q3MjQyZmM5OGZjZDQwMGE4MGRiMjJmMWVmNjc2ZTQwOWFlMTdmNyIsInZlcnNpb24iOjF9.W1bgFs6XhmbeWJlX_6IvWx6MX-yUj5ErdBU1cGAAZRrEA0elBa_-FdbRkwnLDcBNmBm16vtxPAQfQgJQXmIcDA |
| - type: loss |
| value: 2.454127550125122 |
| name: loss |
| verified: true |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTQ0OGMyZGNmZjVlMDYzOTA1NjdlZjZhOThhN2M3ZTZjNWM5N2Y2MjQwZjg4Y2E4MjhiOWUzODFiMzY1YzU0NyIsInZlcnNpb24iOjF9.TOjsyBEWqDD5N9FzJPE9Z7Poj0oXefGryUy7rgj4uXbbWb8DMsMXMcxNVEKixG_vbGyFyASSmgyeW6bAFHaPCw |
| - type: gen_len |
| value: 239.4179 |
| name: gen_len |
| verified: true |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGZmOWY5NmMyNjUzZDM2NmNjNzBjMzU2OTMxYWE2MGFhM2JiMmFmNzQwOTg4NGY5Yzc1NmZjNGZmZjM5NWQzNyIsInZlcnNpb24iOjF9.piE6u39D58dKz2HimpE4Fng7cHELJPuSpZaoEU3gOXSXYw_lx2KQhi2VfFg-mUasmLuQn4bBvMJcWXyBTY8YBw |
| - task: |
| type: summarization |
| name: Summarization |
| dataset: |
| name: big_patent |
| type: big_patent |
| config: y |
| split: test |
| metrics: |
| - type: rouge |
| value: 35.615 |
| name: ROUGE-1 |
| verified: true |
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| --- |
| # pszemraj/long-t5-tglobal-base-16384-booksum-V12 |
|
|
| > this checkpoint has some further training and is **exists separately to confirm metrics before merging to main** |
|
|
| - training metadata data in [this json](training_metadata.json) |
| - the main model can be found [here](https://huggingface.co/pszemraj/long-t5-tglobal-base-16384-book-summary) |
|
|
