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--- |
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license: |
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- apache-2.0 |
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- bsd-3-clause |
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tags: |
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- summarization |
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- summary |
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- booksum |
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- long-document |
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- long-form |
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datasets: |
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- kmfoda/booksum |
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metrics: |
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- rouge |
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languages: en |
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widget: |
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- text: large earthquakes along a given fault segment do not occur at random intervals |
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because it takes time to accumulate the strain energy for the rupture. The rates |
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at which tectonic plates move and accumulate strain at their boundaries are approximately |
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uniform. Therefore, in first approximation, one may expect that large ruptures |
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of the same fault segment will occur at approximately constant time intervals. |
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If subsequent main shocks have different amounts of slip across the fault, then |
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the recurrence time may vary, and the basic idea of periodic mainshocks must be |
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modified. For great plate boundary ruptures the length and slip often vary by |
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a factor of 2. Along the southern segment of the San Andreas fault the recurrence |
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interval is 145 years with variations of several decades. The smaller the standard |
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deviation of the average recurrence interval, the more specific could be the long |
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term prediction of a future mainshock. |
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example_title: earthquakes |
|
- text: ' A typical feed-forward neural field algorithm. Spatiotemporal coordinates |
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are fed into a neural network that predicts values in the reconstructed domain. |
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Then, this domain is mapped to the sensor domain where sensor measurements are |
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available as supervision. Class and Section Problems Addressed Generalization |
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(Section 2) Inverse problems, ill-posed problems, editability; symmetries. Hybrid |
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Representations (Section 3) Computation & memory efficiency, representation capacity, |
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editability: Forward Maps (Section 4) Inverse problems Network Architecture (Section |
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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 |
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maps that transform Or project our domain (e.g, 3D reconstruction via 2D images; |
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Section 4) With appropriate network architecture choices, we can overcome neural |
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network spectral biases (blurriness) and efficiently compute derivatives and integrals |
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(Section 5). Finally, we can manipulate neural fields to add constraints and regularizations, |
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and to achieve editable representations (Section 6). Collectively, these classes |
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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 |
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function € that outputs the conditioning latent variable 2 given an observation |
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0 E(0) =2. 2 is typically a low-dimensional vector, and is often referred to aS |
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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 |
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most probable z given the observations O: argmaxz P(2/0). The decoder maximizes |
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the inverse conditional probability to find the most probable 0 given Z: arg- |
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max P(Olz). We discuss different encoding schemes with different optimality guarantees |
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(Section 2.1.1), both global and local conditioning (Section 2.1.2), and different |
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mapping functions Y (Section 2.1.3) 2. Generalization Suppose we wish to estimate |
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a plausible 3D surface shape given a partial or noisy point cloud. We need a suitable |
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prior over the sur- face in its reconstruction domain to generalize to the partial |
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observations. A neural network expresses a prior via the function space of its |
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architecture and parameters 0, and generalization is influenced by the inductive |
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bias of this function space (Section 5).' |
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example_title: scientific paper |
|
- text: 'Is a else or outside the cob and tree written being of early client rope |
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and you have is for good reasons. On to the ocean in Orange for time. By''s the |
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aggregate we can bed it yet. Why this please pick up on a sort is do and also |
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M Getoi''s nerocos and do rain become you to let so is his brother is made in |
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use and Mjulia''s''s the lay major is aging Masastup coin present sea only of |
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Oosii rooms set to you We do er do we easy this private oliiishs lonthen might |
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be okay. Good afternoon everybody. Welcome to this lecture of Computational Statistics. |
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As you can see, I''m not socially my name is Michael Zelinger. I''m one of the |
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task for this class and you might have already seen me in the first lecture where |
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I made a quick appearance. I''m also going to give the tortillas in the last third |
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of this course. So to give you a little bit about me, I''m a old student here |
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with better Bulman and my research centres on casual inference applied to biomedical |
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disasters, so that could be genomics or that could be hospital data. If any of |
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you is interested in writing a bachelor thesis, a semester paper may be mastathesis |
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about this topic feel for reach out to me. you have my name on models and my email |
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address you can find in the directory I''d Be very happy to talk about it. you |
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do not need to be sure about it, we can just have a chat. So with that said, let''s |
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get on with the lecture. There''s an exciting topic today I''m going to start |
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by sharing some slides with you and later on during the lecture we''ll move to |
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the paper. So bear with me for a few seconds. Well, the projector is starting |
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up. Okay, so let''s get started. Today''s topic is a very important one. It''s |
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about a technique which really forms one of the fundamentals of data science, |
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machine learning, and any sort of modern statistics. It''s called cross validation. |
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I know you really want to understand this topic I Want you to understand this |
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and frankly, nobody''s gonna leave Professor Mineshousen''s class without understanding |
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cross validation. So to set the stage for this, I Want to introduce you to the |
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validation problem in computational statistics. So the problem is the following: |
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You trained a model on available data. You fitted your model, but you know the |
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training data you got could always have been different and some data from the |
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environment. Maybe it''s a random process. You do not really know what it is, |
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but you know that somebody else who gets a different batch of data from the same |
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environment they would get slightly different training data and you do not care |
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that your method performs as well. On this training data. you want to to perform |
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well on other data that you have not seen other data from the same environment. |
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So in other words, the validation problem is you want to quantify the performance |
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of your model on data that you have not seen. So how is this even possible? How |
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could you possibly measure the performance on data that you do not know The solution |
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to? This is the following realization is that given that you have a bunch of data, |
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you were in charge. You get to control how much that your model sees. It works |
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in the following way: You can hide data firms model. Let''s say you have a training |
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data set which is a bunch of doubtless so X eyes are the features those are typically |
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hide and national vector. It''s got more than one dimension for sure. And the |
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why why eyes. Those are the labels for supervised learning. As you''ve seen before, |
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it''s the same set up as we have in regression. And so you have this training |
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data and now you choose that you only use some of those data to fit your model. |
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You''re not going to use everything, you only use some of it the other part you |
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hide from your model. And then you can use this hidden data to do validation from |
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the point of you of your model. This hidden data is complete by unseen. In other |
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words, we solve our problem of validation.' |
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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 |
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checkout 🤗''s recent blog post in case you are unfamiliar with these models. |
|
|
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BigBird (introduced in paper) is one of such recent models to address this issue. |
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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. |
|
|
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BigBird RoBERTa-like model is now available in 🤗Transformers. The goal of this |
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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). |
|
|
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If you wonder why we need more compute when working with longer sequences, this |
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blog post is just right for you! |
|
|
|
Some of the main questions one might have when working with standard BERT-like |
|
attention include: |
|
|
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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. |
|
|
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What tokens should be attended to? We will give a practical example of how attention |
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works by considering the sentence ''BigBird is now available in HuggingFace for |
|
extractive question answering''. In BERT-like attention, every word would simply |
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attend to all other tokens. |
|
|
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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 |
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available is queried and build a sensible list of key tokens to attend to. |
|
|
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>>> # let''s consider following sentence as an example >>> example = [''BigBird'', |
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''is'', ''now'', ''available'', ''in'', ''HuggingFace'', ''for'', ''extractive'', |
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''question'', ''answering''] |
|
|
|
>>> # further let''s assume, we''re trying to understand the representation of |
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''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 |
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to attend Nearby tokens should be important because, in a sentence (sequence of |
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words), the current word is highly dependent on neighboring past & future tokens. |
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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. |
|
😂 |
|
|
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And yes, by the way, i DO have a Rick & Morty tattoo. And no, you cannot see it. |
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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 |
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kid 😎' |
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example_title: Richard & Mortimer |
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parameters: |
|
max_length: 48 |
|
min_length: 2 |
|
no_repeat_ngram_size: 3 |
|
encoder_no_repeat_ngram_size: 3 |
|
early_stopping: true |
|
length_penalty: 0.1 |
|
num_beams: 2 |
|
model-index: |
|
- name: pszemraj/pegasus-x-large-book-summary |
|
results: |
|
- task: |
|
type: summarization |
|
name: Summarization |
|
dataset: |
|
name: samsum |
|
type: samsum |
|
config: samsum |
|
split: test |
|
metrics: |
|
- type: rouge |
|
value: 33.1401 |
|
name: ROUGE-1 |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjQ1NjY1OGVjYWEwMzBjMzk3ZmMyZDA0ZTcxOTdmZTUxNTc0OGYxYmY3MzJkMzFmYTVjNzU2ZTk4MzE0NWMzMSIsInZlcnNpb24iOjF9.PSHB6DMF6tkwSw5nsFE57a2ApRAy_tkS6ziKA6PSTWddEdaqfca4pfig6_olmRmcS4KxN6HHcsmioHzv4LJQBw |
|
- type: rouge |
|
value: 9.3095 |
|
name: ROUGE-2 |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzk3MTA3NmY1OGE3MzFjZTJhYWYzNGU4NTUzMTgwM2Y1NWZjMmEyNDNmNmEzYmQzZThjOGExMjc2ZjAyZjMzZCIsInZlcnNpb24iOjF9.tfgp8p-WlkVrfducTSg4zs-byeZMCmdZw1aizPQHXm_qRAwGtKcuVkZcmza5Y3o3VqsAEmGzg5HQD1vnZvWIDA |
|
- type: rouge |
|
value: 24.8552 |
|
name: ROUGE-L |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTVmMTIwNDQwNTI4MmI2MmY1ODc1Mjk0NGQ5ZWE4ZTYzOGNkMjY2ZmJhMjg2MTZlNTdhYTA2ZDAxNTFjMjA2MSIsInZlcnNpb24iOjF9.9HLgy9842oIDm6ABb3L94R1P4zAqTI0QN8aP62xzIyDxUXTbWw68PEDufYLiBJbTgZ8ElopZ9I7aou2zCgXeAA |
|
- type: rouge |
|
value: 29.0391 |
|
name: ROUGE-LSUM |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMmNhYWJjYjdjMzMxMmE4ZTE4NGEzMDdmZDZjODI5ZWRjZWJmYTEyZGIzYWQ2NjM3YzQ4MjI4ZTM4MmU5MzRjZSIsInZlcnNpb24iOjF9.d2yoVdmxjVJnsgIYFiLuaBO5Krgw4Axl5yeOSTKrvHygrAxoqT1nl4anzQiyoR3PwYBXwBkwmgpJUfZ7RNXtDQ |
|
- type: loss |
|
value: 2.288182497024536 |
|
name: loss |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzM5NGIwODMxOTA3MTY3ODc2ZDczYTNmMTMwM2QyZmNlZjFmZDJjMGY3NWNkMDEyYzA4OTA2ZDRiODY3Zjg4OCIsInZlcnNpb24iOjF9.8k9mC050OS7mQSR9oA8liDRDQvEx1VxmTXGLmDYJVYYtTh2HYJFGP8Vy_krocFRIYDxh-IHPEOOSr5NrLMWHBA |
|
- type: gen_len |
|
value: 45.2173 |
|
name: gen_len |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNWZhNzQ5OTQ5Yjg5YjhlOTZiZmJhZjZiODNmY2E2OTg4YTg4NWVhYzRkNzM2Mzk4NzdlMDgxM2M4NjY2YzhhYSIsInZlcnNpb24iOjF9.tDEEsPUclZDygAdGhNrBGrF24vR8ao08Nw7hmtUt5lmSZZZK_u-8rpz97QgVS6MCJdjFVnbYC4bkFnlQWI_FAA |
|
- task: |
|
type: summarization |
|
name: Summarization |
|
dataset: |
|
name: launch/gov_report |
|
type: launch/gov_report |
|
config: plain_text |
|
split: test |
|
metrics: |
|
- type: rouge |
|
value: 39.7279 |
|
name: ROUGE-1 |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTAxODk3OTUwMTIzODU3NzU2YzAzZjE2NTM3MzBjNDA0ZWRmZGU3NWUzNTg1YThhNDQ1NjQ5ZmM3OWI2YzBhNSIsInZlcnNpb24iOjF9.vnNKucBNt2-nIyODj9P2HeaWPX5AQR8L-DL8QzrO7kj58-vZnjT6hsAGmepRNzdZ1TLF-3j2J2plcNJ8lUO8Dg |
|
- type: rouge |
|
value: 10.8944 |
|
name: ROUGE-2 |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjYzMmIxOTJmZjkxOGI5N2U0NTRmMmQwOGJhMzMxYWIzMWMzYzUwMDEyMDdiZDQ2YTUzOWU0OTViMTI2YTAwYiIsInZlcnNpb24iOjF9.De0PaAikWqfWpoIXTCYP-mSFu3PUATLX08Qq74OHXM8784heFVDX1E1sXlh_QbbKJbuMuZtTKM4qr7oLUizOAw |
|
- type: rouge |
|
value: 19.7018 |
|
name: ROUGE-L |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzI3MjQzOGQ3MGE3NDNkZTEyMWRkYjUyYTYzNDEwOWVjMGFmNTBiZjE4ZTBhMGYzMmI1Yzk0YjBmYmIzMWMxZSIsInZlcnNpb24iOjF9.FVikJ5Ma0gUgM-tpbomWXnC4jtmvhxqikPqCk84t4IbIdU0CIYGTQEONiz-VqI0fJeNrnTS6lxpBv7XxKoq3BQ |
|
- type: rouge |
|
value: 36.5634 |
|
name: ROUGE-LSUM |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTI2OTVmNDZiZWE5ZjNkODIwZjJiNTU2ZjJjYjczODUwM2JiNDEzYmE3N2U5YWM5NzJjOWEzMmYzZjdlYWJmYyIsInZlcnNpb24iOjF9.poR4zcqRvdaierfWFdTa53Cv6ZbNbnRwyRTi9HukHF5AWAQgc6zpBLkwOYFYoWjuSH83ohWeMM3MoIdw3zypBw |
|
- type: loss |
|
value: 2.473011016845703 |
|
name: loss |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDFmMjg3NWQ2YTMxMTc1OGZiYWYzNjg5NDY3MWE4MjY5ZDQxZDZhZGI1OTc5MzZkZGEzYmVlNWFiMzZjNDdhNCIsInZlcnNpb24iOjF9.05nKB3SmEfFKSduJqlleF4Fd2_IhwJS8eTOrnzZYCQQfLCfpJAZLhp3eLQCuBY4htd-FNrZftrThL66zVxyrCQ |
|
- type: gen_len |
|
value: 212.8243 |
|
name: gen_len |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOGNjMTg4ZDZlZjAxZGNhN2M0NWI0ZTA0OWEzNDkzNDAzOTJhODA2MmVkODI4YjYzN2FiOTU1ZDMwM2VlNWMyYyIsInZlcnNpb24iOjF9.WYx6XJFKokY2heoN-jpAMp1Z1gsyJus3zpktQgNd0FOYJxOUqW40A0kkHtd15y4dUhsbccLpuJGY1fNJgHOiDw |
|
- task: |
|
type: summarization |
|
name: Summarization |
|
dataset: |
|
name: billsum |
|
type: billsum |
|
config: default |
|
split: test |
|
metrics: |
|
- type: rouge |
|
value: 42.1065 |
|
name: ROUGE-1 |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDJhNDM2MWEwMjJlYjRmZTVkYzljODcwMzlmMGUxMDA4ZmRjNjM0NmY3ZWJlMmZjNGI3NDQ3NTQyOTQ3MjBkNSIsInZlcnNpb24iOjF9.l1MiZbXyFyXAcsfFChMrTvSaBhzBR6AuDnBuII8zY3Csz3ShWK0vo09MkQdZ1epe8PKWV9wwUBuJyKk3wL7MDw |
|
- type: rouge |
|
value: 15.4079 |
|
name: ROUGE-2 |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTY3NDBkYTVkNjdhY2I0ZmY0NTA4YzVkMGE5YWE5ODdjOGE1MDhkOTJhOWY3NmI2ZWI1MGU2MGI1NDRlYjI3MSIsInZlcnNpb24iOjF9.VN-5eK2SzFDCJnFTHHu7XCU_lynaxW_JEDc3llmcNo_ffDgRmISHHGaqV7fPFymBBMXpPly7XblO_sukyqj1Cg |
|
- type: rouge |
|
value: 24.8814 |
|
name: ROUGE-L |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDYyNGZmNDY3MTY4YzI4ZjZhODE0NGIyN2ZkOGEyYzM3MWZjM2QzZTg5ZjNmZmYzZDE5NzhiZDQ4OGM1YjNiMyIsInZlcnNpb24iOjF9.L73M1M5XdMQkf8zSdfLN0MUrxtO0r6UiLjoOkHfrIGbWNsNJ8tU5lciYFNIhJrICUL8LchCsFqR9LAClKS4bCg |
|
- type: rouge |
|
value: 36.0375 |
|
name: ROUGE-LSUM |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTBlMTQ5OTQxNTA3ZmFiMGYyZWQ0MGM0ODY2YWI3MzgyNjkwNzQyM2FmNGRjMzc3MjJmZDZkOWY4M2RhZTg2MSIsInZlcnNpb24iOjF9.IiMSSVahBgH8n34bGCC_DDGpujDXQbIvGhlcpVV2EBVQLLWUqcCy5WwBdbRrxPC-asBRCNERQxj8Uii4FvPsDQ |
|
- type: loss |
|
value: 1.9130958318710327 |
|
name: loss |
|
verified: true |
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value: 179.2184 |
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name: gen_len |
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verified: true |
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type: summarization |
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name: Summarization |
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dataset: |
|
name: kmfoda/booksum |
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type: kmfoda/booksum |
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config: kmfoda--booksum |
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split: test |
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- task: |
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type: summarization |
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name: Summarization |
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dataset: |
|
name: big_patent |
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type: big_patent |
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config: y |
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split: test |
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--- |
|
|
|
# pszemraj/pegasus-x-large-book-summary |
|
|
|
|
|
<a href="https://colab.research.google.com/gist/pszemraj/6c326c0649233ab017d63adc36958d1a/pegasus-x-large-booksum-demo.ipynb"> |
|
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> |
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</a> |
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|
|
Get SparkNotes-esque summaries of arbitrary text! Due to the model size, it's recommended to try it out in Colab (linked above) as the API textbox may time out. |
|
|
|
This model is a fine-tuned version of [google/pegasus-x-large](https://huggingface.co/google/pegasus-x-large) on the `kmfoda/booksum` dataset for approx eight epochs. |
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|
|
## Model description |
|
|
|
More information needed |
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|
## Intended uses & limitations |
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|
|
- This seems to be the GPU-hungriest summarization model yet. |
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|
## Training and evaluation data |
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|
|
More information needed |
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|
|
## Training procedure |
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|
|
### Training hyperparameters |
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|
|
#### Epochs 1-4 |
|
|
|
TODO |
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|
|
#### Epochs 5 & 6 |
|
The following hyperparameters were used during training: |
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|
|
- learning_rate: 6e-05 |
|
- train_batch_size: 4 |
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- eval_batch_size: 1 |
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- seed: 42 |
|
- distributed_type: multi-GPU |
|
- gradient_accumulation_steps: 32 |
|
- total_train_batch_size: 128 |
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- optimizer: _ADAN_ using lucidrains' `adan-pytorch` with default betas |
|
- lr_scheduler_type: constant_with_warmup |
|
- data type: TF32 |
|
- num_epochs: 2 |
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|
|
#### Epochs 7 & 8 |
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|
|
- epochs 5 & 6 were trained with 12288 tokens input |
|
- this fixes that with 2 epochs at 16384 tokens input |
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|
|
The following hyperparameters were used during training: |
|
- learning_rate: 0.0004 |
|
- train_batch_size: 4 |
|
- eval_batch_size: 1 |
|
- seed: 42 |
|
- distributed_type: multi-GPU |
|
- gradient_accumulation_steps: 16 |
|
- total_train_batch_size: 64 |
|
- optimizer: _ADAN_ using lucidrains' `adan-pytorch` with default betas |
|
- lr_scheduler_type: cosine |
|
- lr_scheduler_warmup_ratio: 0.03 |
|
- num_epochs: 2 |
|
|
|
### Framework versions |
|
|
|
- Transformers 4.22.0 |
|
- Pytorch 1.11.0a0+17540c5 |
|
- Datasets 2.4.0 |
|
- Tokenizers 0.12.1 |
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|
