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README.md

@@ -0,0 +1,363 @@
+---
+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
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+    - 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
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+    - 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
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+    - type: rouge
+      value: 33.4275
+      name: ROUGE-LSUM
+      verified: true
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+    - 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
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+      value: 5.634
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+    - type: rouge
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+    - type: rouge
+      value: 33.0665
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+  - task:
+      type: summarization
+      name: Summarization
+    dataset:
+      name: big_patent
+      type: big_patent
+      config: y
+      split: test
+    metrics:
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+    - type: loss
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+      name: loss
+      verified: true
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+      value: 170.3483
+      name: gen_len
+      verified: true
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiY2MxNWFjYTg1Yjc3YmNjMjViYjM5ZDdmY2NhNjFjMWQxYWQwOWI3NTczY2M5ZWVmMGM2MmQ0ZmY3M2Y0MDEwZiIsInZlcnNpb24iOjF9.J80uRlSZCVIsvyVkO8rqQ4vyZrgBMu1YpOckAzIaj_jTWKGaOPM3kj6sSePiEN8OLZYwDueqLsKkPa0B6ZXIBw
+---
+# 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)

config.json

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+{
+  "_name_or_path": "pszemraj/long-t5-tglobal-base-16384-booksum-V11",
+  "architectures": [
+    "LongT5ForConditionalGeneration"
+  ],
+  "d_ff": 2048,
+  "d_kv": 64,
+  "d_model": 768,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "gelu_new",
+  "dropout_rate": 0.1,
+  "early_stopping": true,
+  "encoder_attention_type": "transient-global",
+  "encoder_no_repeat_ngram_size": 4,
+  "eos_token_id": 1,
+  "feed_forward_proj": "gated-gelu",
+  "global_block_size": 16,
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "length_penalty": 0.8,
+  "local_radius": 127,
+  "max_length": 512,
+  "min_length": 8,
+  "model_type": "longt5",
+  "n_positions": 4096,
+  "no_repeat_ngram_size": 3,
+  "num_beams": 2,
+  "num_decoder_layers": 12,
+  "num_heads": 12,
+  "num_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "repetition_penalty": 3.5,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.22.0.dev0",
+  "use_cache": false,
+  "vocab_size": 32128
+}

long-t5-tglobal-base-16384-booksum-V11-ft25-booksum_training_metadata.json

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training_metadata.json

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+{
+   "output_dir":"/content/drive/MyDrive/Programming/hf-trainer/long-t5-tglobal-base-16384-booksum-V11-ft25-booksum",
+   "overwrite_output_dir":true,
+   "do_train":false,
+   "do_eval":false,
+   "do_predict":false,
+   "evaluation_strategy":"no",
+   "prediction_loss_only":false,
+   "per_device_train_batch_size":2,
+   "per_device_eval_batch_size":1,
+   "per_gpu_train_batch_size":"None",
+   "per_gpu_eval_batch_size":"None",
+   "gradient_accumulation_steps":32,
+   "eval_accumulation_steps":"None",
+   "eval_delay":0,
+   "learning_rate":0.001,
+   "weight_decay":0,
+   "adam_beta1":0.9,
+   "adam_beta2":0.999,
+   "adam_epsilon":1e-08,
+   "max_grad_norm":1,
+   "num_train_epochs":1,
+   "max_steps":-1,
+   "lr_scheduler_type":"constant_with_warmup",
+   "warmup_ratio":0.03,
+   "warmup_steps":0,
+   "log_level":-1,
+   "log_level_replica":-1,
+   "log_on_each_node":true,
+   "logging_dir":"/content/drive/MyDrive/Programming/hf-trainer/long-t5-tglobal-base-16384-booksum-V11-ft25-booksum/logs",
+   "logging_strategy":"steps",
+   "logging_first_step":false,
+   "logging_steps":2,
+   "logging_nan_inf_filter":true,
+   "save_strategy":"steps",
+   "save_steps":25,
+   "save_total_limit":1,
+   "save_on_each_node":false,
+   "no_cuda":false,
+   "use_mps_device":false,
+   "seed":42,
+   "data_seed":"None",
+   "jit_mode_eval":false,
+   "use_ipex":false,
+   "bf16":false,
+   "fp16":false,
+   "fp16_opt_level":"O1",
+   "half_precision_backend":"auto",
+   "bf16_full_eval":false,
+   "fp16_full_eval":false,
+   "tf32":"None",
+   "local_rank":0,
+   "xpu_backend":"None",
+   "tpu_num_cores":"None",
+   "tpu_metrics_debug":false,
+   "debug":"[]",
+   "dataloader_drop_last":false,
+   "eval_steps":"None",
+   "dataloader_num_workers":0,
+   "past_index":-1,
+   "run_name":"/content/drive/MyDrive/Programming/hf-trainer/long-t5-tglobal-base-16384-booksum-V11-ft25-booksum",
+   "disable_tqdm":false,
+   "remove_unused_columns":true,
+   "label_names":"None",
+   "load_best_model_at_end":false,
+   "metric_for_best_model":"None",
+   "greater_is_better":"None",
+   "ignore_data_skip":false,
+   "sharded_ddp":"[]",
+   "fsdp":"[]",
+   "fsdp_min_num_params":0,
+   "fsdp_transformer_layer_cls_to_wrap":"None",
+   "deepspeed":"None",
+   "label_smoothing_factor":0.0,
+   "optim":"adamw_hf",
+   "adafactor":false,
+   "group_by_length":false,
+   "length_column_name":"length",
+   "report_to":"['tensorboard']",
+   "ddp_find_unused_parameters":"None",
+   "ddp_bucket_cap_mb":"None",
+   "dataloader_pin_memory":true,
+   "skip_memory_metrics":false,
+   "use_legacy_prediction_loop":false,
+   "push_to_hub":true,
+   "resume_from_checkpoint":"None",
+   "hub_model_id":"long-t5-tglobal-base-16384-booksum-V11-ft25-booksum",
+   "hub_strategy":"end",
+   "hub_token":"<HUB_TOKEN>",
+   "hub_private_repo":true,
+   "gradient_checkpointing":true,
+   "include_inputs_for_metrics":false,
+   "fp16_backend":"auto",
+   "push_to_hub_model_id":"None",
+   "push_to_hub_organization":"None",
+   "push_to_hub_token":"<PUSH_TO_HUB_TOKEN>",
+   "_n_gpu":1,
+   "mp_parameters":"",
+   "auto_find_batch_size":false,
+   "full_determinism":false,
+   "torchdynamo":"None",
+   "ray_scope":"last",
+   "ddp_timeout":1800,
+   "sortish_sampler":false,
+   "predict_with_generate":false,
+   "generation_max_length":"None",
+   "generation_num_beams":"None",
+   "train_batch_size":2,
+   "eval_batch_size":1,
+   "configs_src":"long-t5-tglobal-base-16384-booksum-V11-ft25-booksum",
+   "use_adam8bit":false,
+   "use_adan_optim":false,
+   "optim_params":"(Adafactor (\nParameter Group 0\n    beta1: None\n    clip_threshold: 1.0\n    decay_rate: -0.8\n    eps: (1e-30, 0.001)\n    lr: 0.0\n    relative_step: True\n    scale_parameter: True\n    warmup_init: True\n    weight_decay: 0.0\n), <transformers.optimization.AdafactorSchedule object at 0x7f6ab0187890>)"
+}