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

README.md

@@ -1,13 +1,13 @@
 ---
+license:
+- apache-2.0
+- bsd-3-clause
 tags:
 - summarization
 - summary
 - booksum
 - long-document
 - long-form
-license:
-- apache-2.0
-- bsd-3-clause
 datasets:
 - kmfoda/booksum
 metrics:
@@ -26,39 +26,38 @@ 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
@@ -110,68 +109,82 @@ 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: 64
@@ -194,30 +207,36 @@ model-index:
       config: samsum
       split: test
     metrics:
-    - name: ROUGE-1
-      type: rouge
+    - type: rouge
       value: 30.0032
+      name: ROUGE-1
       verified: true
-    - name: ROUGE-2
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjk2MTRiNDljZTM4NzliNDdmMTdkZGY3MGY4OTVmMzFhOTdjNGFjYjJhYTBjYTI4Y2VkOGMxYWI5M2M3YWEyZSIsInZlcnNpb24iOjF9.cZtcCwB1Bnnn1g4x8Ia_8oTSK89feGF80r20jwjSb-xy5Xt3eR3dOVjJyjurfN0UOGyEe7inTpneJhcAoRwwBg
+    - type: rouge
       value: 7.2671
+      name: ROUGE-2
       verified: true
-    - name: ROUGE-L
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNThiYmJhN2NkYmU0MmZmZGY5MGU2NmEzZGQwNjM0MDEwNzlhNDgzY2E2MzkxMWVkZTUwMWFlZmFhYWEwN2M5ZSIsInZlcnNpb24iOjF9.IaaaHiOxUdh6IDGbb2vCCEcL-YhXCtaFlZnIpcgQwsC3KRgfrpQi5vdhyaaIJSieA2pzbFjUO--WqjylvpysCA
+    - type: rouge
       value: 21.8779
+      name: ROUGE-L
       verified: true
-    - name: ROUGE-LSUM
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTc1N2YwODk4YmU1Mjk3NGQ2ZDVkYWVjN2Y1ZDVlOTNkMjU5MjcyYjY0ZWY5NjJkNzZjNjMwZWUxNWY0NTY1ZiIsInZlcnNpb24iOjF9.HhYA0t2Ee3YhtBDPneU7hzEEz5c4FeBcTo-3TSSClltG3A5E3RIgbxUbQNbldRAL9Y44Z8uzEHfe676eL22vBg
+    - type: rouge
       value: 26.4371
+      name: ROUGE-LSUM
       verified: true
-    - name: loss
-      type: loss
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTJmZmJhZTBiZDczYmNkNWQ0MGQ3ZTIyNzc2NGExMGY1MGNkOThlNDg0OWQ3YWFmNDRmYTUxZTYzN2U5Yzc4MCIsInZlcnNpb24iOjF9.fgr8NNlhDCvtXMudOce1pf_slujIhXAEC3a6fH6AAlgIvzxg1oGV5QiUcrPDNhyFD2XazZ39Xk1GhoMk4AnxAQ
+    - type: loss
       value: 2.6383285522460938
+      name: loss
       verified: true
-    - name: gen_len
-      type: gen_len
+      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
@@ -227,30 +246,36 @@ model-index:
       config: plain_text
       split: test
     metrics:
-    - name: ROUGE-1
-      type: rouge
+    - type: rouge
       value: 37.0538
+      name: ROUGE-1
       verified: true
-    - name: ROUGE-2
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzViY2Y2ZWIwMDdhNDEzMDU3MmE4ZTBlZjQ2MDI2YTVjOGZjZDM5NzhiZDk2MWJhZWY5MDUwY2NhZTY2OTc5ZSIsInZlcnNpb24iOjF9.p2z_oZD9uVTnBtf7vRRKvisW-rXWVibpU0QQ-S_16CIYLc2kTJRZMLzaMJqbi1d8icBTeG5PdIzKcAVwu7JKCA
+    - type: rouge
       value: 8.1512
+      name: ROUGE-2
       verified: true
-    - name: ROUGE-L
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWUzZGM0ZGJiMDYwM2ZmYjI5Mzk5MTU2N2JlZGVlOGRjMTJjY2QwOWIwMjgyMjM0ZjIzY2Q4MzJjNDkxZmVhMCIsInZlcnNpb24iOjF9.z6pMF8l4uMQIEcdyU1kgDc1v3rCn-0TVxntKP3hmOEwRJqfbeqDmhhAROWadYTPNewpfsCpShVHGJt9DvH55BQ
+    - type: rouge
       value: 17.6645
+      name: ROUGE-L
       verified: true
-    - name: ROUGE-LSUM
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZWNkYzY2NGY4YmFiNWRhODAwZmFmOTkzM2M3MGY0ZTQzZTUwNmExNDc5ZDdhZWVhZjFhYTUyYjFlZjQ3ZDA4ZCIsInZlcnNpb24iOjF9.XbVCDhR_l7OalwF2DsHJSZ39z_HHdG3PlwKL0Ls9lBvRo4E8sk00vrQy4IRCqPF8hPJusl2Nb65V3CvgIldqAA
+    - type: rouge
       value: 33.4275
+      name: ROUGE-LSUM
       verified: true
-    - name: loss
-      type: loss
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDdiYzI0MDlmYjg0MWFjZDBmMmIyZWUyNzNhYTUyNTU1ZDdhODE4ZTlmMTg5MDY1MDhhMGRlMGU1OTA3YzM4ZSIsInZlcnNpb24iOjF9.pDHKUDMXHihmLSQzYq6bxclcLyajcRf6Q5ImhpvpoepG8du5ggwb1q_2anGfDjJ0kkFa-Iwtbl8KmdqD7TTCAQ
+    - type: loss
       value: 2.6052205562591553
+      name: loss
       verified: true
-    - name: gen_len
-      type: gen_len
+      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
@@ -260,30 +285,36 @@ model-index:
       config: kmfoda--booksum
       split: test
     metrics:
-    - name: ROUGE-1
-      type: rouge
+    - type: rouge
       value: 36.1423
+      name: ROUGE-1
       verified: true
-    - name: ROUGE-2
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTZkYTA5N2FhNjVhMzg1ZDRjOThhZjcwMjdmYzQ1MGE5N2RhNTM0MmNjMzVkYjNlYmZjOGZjMDFlZDBkMGM5MSIsInZlcnNpb24iOjF9.odQ-NMcQ06o2mqzXOfGY1c967_RUfg93YfGnMTpKUXPM5dGawkdVYGO8rPCHt5bttPvYlBmRgNl6Z7H_OhgnCA
+    - type: rouge
       value: 5.634
+      name: ROUGE-2
       verified: true
-    - name: ROUGE-L
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZmFkODViOTg2MDYxZDhlMjZiOTNjZWE2ZTI5YmVhYWRiNGM1OTAzZDEzN2Y1ODI4OWI3NzU2ZmZlMGJjNGIyZiIsInZlcnNpb24iOjF9.4-VpnxVDiC0AG-de1dFr6VHNNbK2qZhAMQ62EpVU7Et-n25w8GPcoyr9l4AXIodQpU6p0H0pdntEUqQwJOHaDg
+    - type: rouge
       value: 16.3747
+      name: ROUGE-L
       verified: true
-    - name: ROUGE-LSUM
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzkzYWY1NmEyMWNkODQ2N2ExYzMwNWExZDgwNTkxMTg5OTNjYjU5NjMwNWU3NzZhZDYwYzA4M2I0ZmU3Yjg2NiIsInZlcnNpb24iOjF9.tY2mQ0bZU9GMYYTJPot_vgvmiAoubdYWAzEQSQskigleh7AWtsXbO2CnhBsE_7UpsLPVWGccP0IWkHdHRg9zAA
+    - type: rouge
       value: 33.0665
+      name: ROUGE-LSUM
       verified: true
-    - name: loss
-      type: loss
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTEyZGZlNmRhNjllMGExZTJhOWE0NDQwN2Q3MjQyZmM5OGZjZDQwMGE4MGRiMjJmMWVmNjc2ZTQwOWFlMTdmNyIsInZlcnNpb24iOjF9.W1bgFs6XhmbeWJlX_6IvWx6MX-yUj5ErdBU1cGAAZRrEA0elBa_-FdbRkwnLDcBNmBm16vtxPAQfQgJQXmIcDA
+    - type: loss
       value: 2.454127550125122
+      name: loss
       verified: true
-    - name: gen_len
-      type: gen_len
+      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
@@ -293,30 +324,36 @@ model-index:
       config: y
       split: test
     metrics:
-    - name: ROUGE-1
-      type: rouge
+    - type: rouge
       value: 35.615
+      name: ROUGE-1
       verified: true
-    - name: ROUGE-2
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWM4ZWQxMjBmNzFlYWMwODg5YTEzOWRmYzBiNmI4ZjBmNmFiZjk2NWQxNDFmY2QzNTA3ZTc5ODZkNmJkZGE4NSIsInZlcnNpb24iOjF9.MABjYbSyTQrT0QxzXM9VRpdDb5dchk1GI_TD_NSB27ozZdWEXyZ-dp44jR-M9mJTSsGk60czxmCF1gq-e4YhAQ
+    - type: rouge
       value: 8.2625
+      name: ROUGE-2
       verified: true
-    - name: ROUGE-L
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTk3MmI3ZmQyOTlmYzc4YTkwNjBjOTM3YmE5NjQxOGVkMDFlODc4YjgxMzlhNGRkYThkMzQ5OTU4YWFjYTg0NiIsInZlcnNpb24iOjF9.KHipwLhPWwc55GQpvNe3bSrKOgaAs4sFvLEGvzVa4HWWyvz4oX2ZaytYnURH9Xid7d9nTr7zWYYiwQ7TmSXPDA
+    - type: rouge
       value: 19.9883
+      name: ROUGE-L
       verified: true
-    - name: ROUGE-LSUM
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTlhZDk5ZmEyYzgxY2IyNWI1MTk1Nzg2YmVlNmRhMjcyZmFmMWZkNGQ4OWEwYjQwYTk3YzllODdiNzRkN2M5ZCIsInZlcnNpb24iOjF9.ah1-tJ5rUuUToNUHUMf9v9_TGJdhffBMdPDthvo3fmKcFtUQFAMwIloGLp0ePcCS_h8IMEyrtpMwqcDc7jrgAw
+    - type: rouge
       value: 30.1801
+      name: ROUGE-LSUM
       verified: true
-    - name: loss
-      type: loss
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzViMzBiY2I2NWNkMjJmMmZhOTk2YzY3NTFhZTIxOTAzY2ZmNmJlYTlmZDI4YjAyYmRiNDRlNTk0MWJjMmY1MCIsInZlcnNpb24iOjF9.KUPyHMK77clPtJHyXR5WirKcy5O5hZP-MBZE-gFRy21S_sIsHpZNnBuGTJ6AMVi_38MNvDgLQWwSE-4y9eG8Dg
+    - type: loss
       value: 2.8106656074523926
+      name: loss
       verified: true
-    - name: gen_len
-      type: gen_len
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjA1ZTk2NzA5NDUwMjQ1ZDcxZTA0ZTA3YzdjYzhhZWM1ZjI3MTllYTg2YzAxOTk0Nzk1Yjc0OTRiNzIyOWExZSIsInZlcnNpb24iOjF9.q2sdYyFeFxpjGPKGpJDnoOmzTznwA1Z99GBWOHA-9YUI5q_w_kbV8JdfbiQ9GsaN8EqDlmkCL2kv5lC3xvvUAA
+    - type: gen_len
       value: 170.3483
+      name: gen_len
       verified: true
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiY2MxNWFjYTg1Yjc3YmNjMjViYjM5ZDdmY2NhNjFjMWQxYWQwOWI3NTczY2M5ZWVmMGM2MmQ0ZmY3M2Y0MDEwZiIsInZlcnNpb24iOjF9.J80uRlSZCVIsvyVkO8rqQ4vyZrgBMu1YpOckAzIaj_jTWKGaOPM3kj6sSePiEN8OLZYwDueqLsKkPa0B6ZXIBw
 ---
 # pszemraj/long-t5-tglobal-base-16384-booksum-V12