Add verifyToken field to verify evaluation results are produced by Hugging Face's automatic model evaluator

Beep boop, I am a bot from Hugging Face's automatic model evaluator 👋! We've added a new `verifyToken` field to your evaluation results to verify that they are produced by the model evaluator. Accept this PR to ensure that your results remain listed as **verified** on the [Hub leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards).

README.md

@@ -1,18 +1,18 @@
 ---
-languages: en
 license:
 - apache-2.0
 - bsd-3-clause
-datasets:
-- kmfoda/booksum
 tags:
 - summarization
 - summary
 - booksum
 - long-document
 - long-form
+datasets:
+- kmfoda/booksum
 metrics:
 - rouge
+languages: en
 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
@@ -27,39 +27,38 @@ widget:
     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 \u20AC 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 \u20AC 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)."
+- 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
@@ -111,68 +110,82 @@ widget:
     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 \U0001F917's recent blog post in case you are unfamiliar with these\
-    \ models.\nBigBird (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.\nBigBird RoBERTa-like model is now available in \U0001F917\
-    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 \U0001F917\
-    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 \u221E compute & \u221E time, BERT's attention\
-    \ would be preferred over block sparse attention (which we are going to discuss\
-    \ in this post).\nIf you wonder why we need more compute when working with longer\
-    \ sequences, this blog post is just right for you!\nSome of the main questions\
-    \ one might have when working with standard BERT-like attention include:\nDo 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.\nWhat 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.\nLet'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.\n>>> # let's\
-    \ consider following sentence as an example >>> example = ['BigBird', 'is', 'now',\
-    \ 'available', 'in', 'HuggingFace', 'for', 'extractive', 'question', 'answering']\n\
-    >>> # 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."
+- 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. \U0001F602\nAnd 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 \U0001F60E"
+- 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: 48
@@ -194,30 +207,36 @@ model-index:
       config: samsum
       split: test
     metrics:
-    - name: ROUGE-1
-      type: rouge
+    - type: rouge
       value: 33.1401
+      name: ROUGE-1
       verified: true
-    - name: ROUGE-2
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjQ1NjY1OGVjYWEwMzBjMzk3ZmMyZDA0ZTcxOTdmZTUxNTc0OGYxYmY3MzJkMzFmYTVjNzU2ZTk4MzE0NWMzMSIsInZlcnNpb24iOjF9.PSHB6DMF6tkwSw5nsFE57a2ApRAy_tkS6ziKA6PSTWddEdaqfca4pfig6_olmRmcS4KxN6HHcsmioHzv4LJQBw
+    - type: rouge
       value: 9.3095
+      name: ROUGE-2
       verified: true
-    - name: ROUGE-L
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzk3MTA3NmY1OGE3MzFjZTJhYWYzNGU4NTUzMTgwM2Y1NWZjMmEyNDNmNmEzYmQzZThjOGExMjc2ZjAyZjMzZCIsInZlcnNpb24iOjF9.tfgp8p-WlkVrfducTSg4zs-byeZMCmdZw1aizPQHXm_qRAwGtKcuVkZcmza5Y3o3VqsAEmGzg5HQD1vnZvWIDA
+    - type: rouge
       value: 24.8552
+      name: ROUGE-L
       verified: true
-    - name: ROUGE-LSUM
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTVmMTIwNDQwNTI4MmI2MmY1ODc1Mjk0NGQ5ZWE4ZTYzOGNkMjY2ZmJhMjg2MTZlNTdhYTA2ZDAxNTFjMjA2MSIsInZlcnNpb24iOjF9.9HLgy9842oIDm6ABb3L94R1P4zAqTI0QN8aP62xzIyDxUXTbWw68PEDufYLiBJbTgZ8ElopZ9I7aou2zCgXeAA
+    - type: rouge
       value: 29.0391
+      name: ROUGE-LSUM
       verified: true
-    - name: loss
-      type: loss
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMmNhYWJjYjdjMzMxMmE4ZTE4NGEzMDdmZDZjODI5ZWRjZWJmYTEyZGIzYWQ2NjM3YzQ4MjI4ZTM4MmU5MzRjZSIsInZlcnNpb24iOjF9.d2yoVdmxjVJnsgIYFiLuaBO5Krgw4Axl5yeOSTKrvHygrAxoqT1nl4anzQiyoR3PwYBXwBkwmgpJUfZ7RNXtDQ
+    - type: loss
       value: 2.288182497024536
+      name: loss
       verified: true
-    - name: gen_len
-      type: gen_len
+      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
@@ -227,30 +246,36 @@ model-index:
       config: plain_text
       split: test
     metrics:
-    - name: ROUGE-1
-      type: rouge
+    - type: rouge
       value: 39.7279
+      name: ROUGE-1
       verified: true
-    - name: ROUGE-2
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTAxODk3OTUwMTIzODU3NzU2YzAzZjE2NTM3MzBjNDA0ZWRmZGU3NWUzNTg1YThhNDQ1NjQ5ZmM3OWI2YzBhNSIsInZlcnNpb24iOjF9.vnNKucBNt2-nIyODj9P2HeaWPX5AQR8L-DL8QzrO7kj58-vZnjT6hsAGmepRNzdZ1TLF-3j2J2plcNJ8lUO8Dg
+    - type: rouge
       value: 10.8944
+      name: ROUGE-2
       verified: true
-    - name: ROUGE-L
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjYzMmIxOTJmZjkxOGI5N2U0NTRmMmQwOGJhMzMxYWIzMWMzYzUwMDEyMDdiZDQ2YTUzOWU0OTViMTI2YTAwYiIsInZlcnNpb24iOjF9.De0PaAikWqfWpoIXTCYP-mSFu3PUATLX08Qq74OHXM8784heFVDX1E1sXlh_QbbKJbuMuZtTKM4qr7oLUizOAw
+    - type: rouge
       value: 19.7018
+      name: ROUGE-L
       verified: true
-    - name: ROUGE-LSUM
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzI3MjQzOGQ3MGE3NDNkZTEyMWRkYjUyYTYzNDEwOWVjMGFmNTBiZjE4ZTBhMGYzMmI1Yzk0YjBmYmIzMWMxZSIsInZlcnNpb24iOjF9.FVikJ5Ma0gUgM-tpbomWXnC4jtmvhxqikPqCk84t4IbIdU0CIYGTQEONiz-VqI0fJeNrnTS6lxpBv7XxKoq3BQ
+    - type: rouge
       value: 36.5634
+      name: ROUGE-LSUM
       verified: true
-    - name: loss
-      type: loss
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTI2OTVmNDZiZWE5ZjNkODIwZjJiNTU2ZjJjYjczODUwM2JiNDEzYmE3N2U5YWM5NzJjOWEzMmYzZjdlYWJmYyIsInZlcnNpb24iOjF9.poR4zcqRvdaierfWFdTa53Cv6ZbNbnRwyRTi9HukHF5AWAQgc6zpBLkwOYFYoWjuSH83ohWeMM3MoIdw3zypBw
+    - type: loss
       value: 2.473011016845703
+      name: loss
       verified: true
-    - name: gen_len
-      type: gen_len
+      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
@@ -260,30 +285,36 @@ model-index:
       config: default
       split: test
     metrics:
-    - name: ROUGE-1
-      type: rouge
+    - type: rouge
       value: 42.1065
+      name: ROUGE-1
       verified: true
-    - name: ROUGE-2
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDJhNDM2MWEwMjJlYjRmZTVkYzljODcwMzlmMGUxMDA4ZmRjNjM0NmY3ZWJlMmZjNGI3NDQ3NTQyOTQ3MjBkNSIsInZlcnNpb24iOjF9.l1MiZbXyFyXAcsfFChMrTvSaBhzBR6AuDnBuII8zY3Csz3ShWK0vo09MkQdZ1epe8PKWV9wwUBuJyKk3wL7MDw
+    - type: rouge
       value: 15.4079
+      name: ROUGE-2
       verified: true
-    - name: ROUGE-L
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTY3NDBkYTVkNjdhY2I0ZmY0NTA4YzVkMGE5YWE5ODdjOGE1MDhkOTJhOWY3NmI2ZWI1MGU2MGI1NDRlYjI3MSIsInZlcnNpb24iOjF9.VN-5eK2SzFDCJnFTHHu7XCU_lynaxW_JEDc3llmcNo_ffDgRmISHHGaqV7fPFymBBMXpPly7XblO_sukyqj1Cg
+    - type: rouge
       value: 24.8814
+      name: ROUGE-L
       verified: true
-    - name: ROUGE-LSUM
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDYyNGZmNDY3MTY4YzI4ZjZhODE0NGIyN2ZkOGEyYzM3MWZjM2QzZTg5ZjNmZmYzZDE5NzhiZDQ4OGM1YjNiMyIsInZlcnNpb24iOjF9.L73M1M5XdMQkf8zSdfLN0MUrxtO0r6UiLjoOkHfrIGbWNsNJ8tU5lciYFNIhJrICUL8LchCsFqR9LAClKS4bCg
+    - type: rouge
       value: 36.0375
+      name: ROUGE-LSUM
       verified: true
-    - name: loss
-      type: loss
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTBlMTQ5OTQxNTA3ZmFiMGYyZWQ0MGM0ODY2YWI3MzgyNjkwNzQyM2FmNGRjMzc3MjJmZDZkOWY4M2RhZTg2MSIsInZlcnNpb24iOjF9.IiMSSVahBgH8n34bGCC_DDGpujDXQbIvGhlcpVV2EBVQLLWUqcCy5WwBdbRrxPC-asBRCNERQxj8Uii4FvPsDQ
+    - type: loss
       value: 1.9130958318710327
+      name: loss
       verified: true
-    - name: gen_len
-      type: gen_len
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTg2NTMxZDE3MDg3MDFkMTYxNjY1OTc5YjQ4ODcyMGUxMTFiZjJiNDgyYWZhN2NjZmE1MDQ1NTRmZGY0NjQzZSIsInZlcnNpb24iOjF9.kADUBMO8i6-oGDDt1cOiGMrGcMkF_Qc1jSpS2NSFyksDRusQa_YuuShefF4DuHVEr3CS0hNjjRH9_JBeX9ZQDg
+    - type: gen_len
       value: 179.2184
+      name: gen_len
       verified: true
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjM4NGNiMTY3YzZjMzg4MTRiMDdiZDFiMzA1ZDIyMDM2MDk1OWRhYWQzN2UxZDNlODIxOWVhY2JlYjk4Mjk5YyIsInZlcnNpb24iOjF9.nU8ImMNWgjg9BKjUBJQLFaJOBq3kyIne8ldlpL0OV0e4888wOntIAcJP0dCCYfRSLVmZuXQ1M8cpDuTf50hNCw
   - task:
       type: summarization
       name: Summarization
@@ -293,30 +324,36 @@ model-index:
       config: kmfoda--booksum
       split: test
     metrics:
-    - name: ROUGE-1
-      type: rouge
+    - type: rouge
       value: 35.2154
+      name: ROUGE-1
       verified: true
-    - name: ROUGE-2
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZWQ5MGMzNDc4MDBiNmRiNDY5ZDM4N2QzYTJlYTNiYTcwNDBlMzdlM2I4N2VmM2ZjMmQ3NGU3OTRlMTMzMTg3NyIsInZlcnNpb24iOjF9.E55gu7HvMwc4HejF3YOD6yqQJj7_6GCoCMWm78sY5_w2glR-oM98tu9IsG27VaPva7UklxsspzT2DIVaVKY0CQ
+    - type: rouge
       value: 6.8702
+      name: ROUGE-2
       verified: true
-    - name: ROUGE-L
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZjFhN2JlYzlmMGZmYzkwYjBlNjY4YzhlYzNmMTdmZWYyYmU3NWI0ZTRkMTgxNmRiM2EyZWMyMWFjY2JkNzg1MCIsInZlcnNpb24iOjF9.I9BoHbGt8LLNtLAssIXm9tQ4lHqFCMt0zJS_zTezzxGRMS5On71c3jnlzrDtwEm6wjmZEwYIJK8qqJh-Qa5YAA
+    - type: rouge
       value: 17.6693
+      name: ROUGE-L
       verified: true
-    - name: ROUGE-LSUM
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOGZlZjcwOTZjMmNjZWFkM2M5Zjg1OTgzMzcxOTM2Y2RkMzY4NGU2NDE2MTVjMjcyMWIwNWI4ODc0YTY3YTA2MSIsInZlcnNpb24iOjF9.Ou1C6U6PrOtXPxlk9PMucdJ_vlnVnSk94QrLJL4b_g2pcY3D80Xrw09iz4BTOPzZ2UTNBLyn8YdLY3m2vHpiAQ
+    - type: rouge
       value: 32.8365
+      name: ROUGE-LSUM
       verified: true
-    - name: loss
-      type: loss
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMmIzMGQ5MzQ1MjI4MTU0ZGZkZTRhODllNWQyOTQ4ZjA5YWE4ZTJjMzQ2ZWQzOGFiMWUzZDMxOTU5NzkxYjliZiIsInZlcnNpb24iOjF9.2mYURQZYo7e3AY0tfkpqFMNhoHvrysvBXza-XYYrX_xLpruMU9Gzrwc3jvpi2wtp4eeyhzIiZJvH0O6la6zxCg
+    - type: loss
       value: 2.9878039360046387
+      name: loss
       verified: true
-    - name: gen_len
-      type: gen_len
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGU0ODBmN2I3OGFkNTFiM2I3YWQyNmUzNzUwYzEwNzczZWEwZjIxYTAwZDE2ZTIwMGE3ZGNmMDQzNTFmNjEwYyIsInZlcnNpb24iOjF9.0IKWIImKTXqysQUb2IMPk2eeHlOcBjndiPcU42nfFBMhRTqeXdBqOCP6cidlho7pVN4hsC-77ArJ9pZlbTFuBg
+    - type: gen_len
       value: 200.6785
+      name: gen_len
       verified: true
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDUzYTE3MmIxZGM3MWI1MjNhMTU3MTdkMjJjNjY5Y2UzYTdjYWRiY2I4MmUxMDY4NTA5NWZjYWU0NzliODdkYiIsInZlcnNpb24iOjF9.BqmCaWzbCMNUied6zNO744Dl-0LC47FCIv-l8kDjkhSkwQcb_hi93VYts5PTsrFY_MmM8j7AsY1PiFr6nNFMBQ
   - task:
       type: summarization
       name: Summarization
@@ -326,30 +363,36 @@ model-index:
       config: y
       split: test
     metrics:
-    - name: ROUGE-1
-      type: rouge
+    - type: rouge
       value: 37.376
+      name: ROUGE-1
       verified: true
-    - name: ROUGE-2
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWI4ZjMxODcxMThiMzE3NjQ3Zjg0NzhmZjlhY2ZmYjQwMGY5ZjlkZGY1MzZmY2M5YTU4NmY1Y2NhZDA3YWFkOCIsInZlcnNpb24iOjF9.sYh4IynXgOpVetYYSWUp0v5QZWvXC1x7_uJR0LZUxaeYKEc4yfICNmDOPzNzoroaV4ELeOaPjHQpYVm-lpAHBA
+    - type: rouge
       value: 11.4432
+      name: ROUGE-2
       verified: true
-    - name: ROUGE-L
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTZkOGIyYzU3YTQ5ZTFmMDU3MjQ5ZWM2NGQ1MzgwMDYyZDkxN2Q2YjgyZTkzMTEyYjczMGJiYmNkZmU5MTQ3NSIsInZlcnNpb24iOjF9.Qk38acpjPjU64Z1nXEuqMXjKZrGvdC9oY586EjuCPeEAJCSzKimp8FsB-1QrjMH73q6rN2CdumJUxih6HF-KAA
+    - type: rouge
       value: 22.2754
+      name: ROUGE-L
       verified: true
-    - name: ROUGE-LSUM
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzlmOTUxYmEzYzYyYmVjNGZlNzNiZWIwZmQ5OWVlY2U3NTBiZDExYWUwODQ0Y2ZjMmQyMTNmMTlmNjdmZWUwNCIsInZlcnNpb24iOjF9.bUVhxaepySyaityby71j6h4YO_l4x8OSeZoblagwUMYGXRc0Ej286QzEtZFeRGygMJ5sjUN_loWCtOmAnHY2BA
+    - type: rouge
       value: 32.5087
+      name: ROUGE-LSUM
       verified: true
-    - name: loss
-      type: loss
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDEyNjM5NjAzYTNjN2MwZTY4MWY2Y2U5YWUyM2Y1YjAyNjBhZTM0YTAyZjM5N2M1ZDkxOWUxNzE2OWZkYTBmMSIsInZlcnNpb24iOjF9.QfMHkcoAR3xqzsgL1xjHk3Lui1xhE12pJKvYujQ_h5o6PBXT79dsENsrqDGGBjiKdTKNwWqADgaviy1VrWMDCQ
+    - type: loss
       value: 2.9867310523986816
+      name: loss
       verified: true
-    - name: gen_len
-      type: gen_len
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTUzM2Q5MmE5MzU4YmFlMjFiMmUzZGU2NDAzMTQ1Y2NjZDVlYWI3NGE5MjM0NmMxMjdiOWI3MTU0NDk3NmNkZiIsInZlcnNpb24iOjF9.VoQqu6ZU3AR_cji82UkpvbLnTmZ17fZmR2E4DeonjCyTZpyyfvUsQ2nbKDovQf34DBkYXENk42EUsUF1mBZNBg
+    - type: gen_len
       value: 172.7776
+      name: gen_len
       verified: true
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTEzNTMyMDY1N2Q5ZTMxNjNlMTI0Nzk5ZDc1ZWQ5Y2IwZWM0NWNhNWY2MTk3YTRkYzUwMTI4NjZiOWVhOGQwYSIsInZlcnNpb24iOjF9.-Rek2VFmGqIEgqeFoxU_0aCWdFbGYi9BV5c7x-izm9_4vtZdYQ4ITXm4T8C3UlpOax60veJQt2Uax5vyiFc9Ag
 ---
 
 # pszemraj/pegasus-x-large-book-summary