Add verifyToken field to verify evaluation results are produced by Hugging Face's automatic model evaluator (#9)

- Add verifyToken field to verify evaluation results are produced by Hugging Face's automatic model evaluator (65e5f44f8f255ad97728729e93c6863ed1fde416)


Co-authored-by: Evaluation Bot <autoevaluator@users.noreply.huggingface.co>

README.md

@@ -1,4 +1,7 @@
 ---
+license:
+- apache-2.0
+- bsd-3-clause
 tags:
 - summarization
 - led
@@ -7,9 +10,6 @@ tags:
 - booksum
 - long-document
 - long-form
-license:
-- apache-2.0
-- bsd-3-clause
 datasets:
 - kmfoda/booksum
 metrics:
@@ -28,39 +28,38 @@ 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: ' the big variety of data coming from diverse sources is one of the key properties
     of the big data phenomenon. It is, therefore, beneficial to understand how data
@@ -105,50 +104,62 @@ widget:
     in their business An important area of data analytics on the edge of corporate
     IT and the Internet is Web Analytics.'
   example_title: data science textbook
-- 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: 'The majority of available text summarization datasets include short-form
     source documents that lack long-range causal and temporal dependencies, and often
@@ -186,30 +197,36 @@ model-index:
       config: kmfoda--booksum
       split: test
     metrics:
-    - name: ROUGE-1
-      type: rouge
+    - type: rouge
       value: 33.4536
+      name: ROUGE-1
       verified: true
-    - name: ROUGE-2
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYmEzYjNkZTUxZjA0YTdmNTJkMjVkMTg2NDRjNTkzN2ZlNDlhNTBhMWQ5MTNiYWE4Mzg5YTMyMTM5YmZjNDI3OSIsInZlcnNpb24iOjF9.OWjM_HCQLQHK4AV4em70QGT3lrVk25WyZdcXA8ywest_XSx9KehJbsIMDKtXxOOMwxvkogKnScy4tbskYMQqDg
+    - type: rouge
       value: 5.2232
+      name: ROUGE-2
       verified: true
-    - name: ROUGE-L
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTVhOTdjZjc5YTdhMmVjZGE1NTA5MmJkYmM3Y2U3OGVlMjZmOGVlMTUzYTdiZGRhM2NmZjAzMjFkZjlkMzJmOCIsInZlcnNpb24iOjF9.qOlwWEe8dfBunmwImhbkcxzUW3ml-ESsuxjWN1fjn_o36zaUlDqlrXovMcL9GX9mVdvZDhx9W82rAR8h6410AQ
+    - type: rouge
       value: 16.2044
+      name: ROUGE-L
       verified: true
-    - name: ROUGE-LSUM
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzkwOTEwYjkxYzlhMWE4ZjhlZDVjZWEwMWY2YzgwY2Q2YzJkYWFhMTQ4ODFlZmVkY2I1OWVhMTFmZThlOGY4NCIsInZlcnNpb24iOjF9.fJSr9wRQ07YIPMpb2_xv14EkHRz3gsPdZH-4LzpdviLOjVhlK1Y4gSZjp3PTEbu4Hua0umvNTMrhii8hp3DFBA
+    - type: rouge
       value: 29.9765
+      name: ROUGE-LSUM
       verified: true
-    - name: loss
-      type: loss
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYWRkYjcwMTYwODRjN2E4MDliZWQyNjczNDU1NGZkMDRkNDlhNDA1YzZiOTk1MWJjZDkyMDg3MGMxYmVhOTA5MyIsInZlcnNpb24iOjF9.tUkVmhT0bl9eY_BzAzdzEI1lo3Iyfv6HBrrsVsRHqPFh4C0Q9Zk3IXbR-F_gMDx9vDiZIkpfG7SfsIZXwhDkBw
+    - type: loss
       value: 3.1985862255096436
+      name: loss
       verified: true
-    - name: gen_len
-      type: gen_len
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2RmYzQ1NTFiYjk3YTZjMTI3NDJlMDY0MTgyZDZlZDRmZDcwOWE1YjU0OGYyZTJlY2RkZTEzZDFlNDk2ZjgyNSIsInZlcnNpb24iOjF9.Pc5Tfu8IXYeB5ETK2JMIL4gpRIvvYXVS6w1AZdfq9dD1dm9Te2xaNhzGBHviqgEfFI9APNSJB28wna1OpYP0Dg
+    - type: gen_len
       value: 191.9783
+      name: gen_len
       verified: true
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNmMyMDI5MzFlNzNjODNmOWQ0ZTM3MzVkNTNkYzIxNTIwZDQzMTU2MTM0YjYzNjJiMGRhOTQ0OWFhN2U4N2NjYyIsInZlcnNpb24iOjF9.AfsX-O1YwfbPxUwAD7rd1Ub7SXth7FFpTo2iNSOUWFhYmDUECkf6qtJ5pVHXXZwnpidAlfPTPg-5y3dx_BBGCA
   - task:
       type: summarization
       name: Summarization
@@ -219,30 +236,36 @@ model-index:
       config: samsum
       split: test
     metrics:
-    - name: ROUGE-1
-      type: rouge
-      value: 32.0
+    - type: rouge
+      value: 32
+      name: ROUGE-1
       verified: true
-    - name: ROUGE-2
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYmNhZjk3NjFlZDBhZjU2YzgzOTdhZTNkZjBkYjNjZDk2YjE2NDBmMDhiY2Y5M2EwNGI5Njk1NWU3ZDYyMzk2ZSIsInZlcnNpb24iOjF9.htkMQQLjIeFFjnpAJOwwxAdgzGZX10Und6RONubeeydXqQqb562EHqAw0K1ZlqltC4GBGKK3xslGOWXQ5AV6CA
+    - type: rouge
       value: 10.0781
+      name: ROUGE-2
       verified: true
-    - name: ROUGE-L
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWYzZDA1YmU5YTkzMjEwN2IzMTNhZmZmOTU2ZGUyNzdlNWQ0OGQ1Y2UxOGQ0NWUyOWVmZmZkYzFkODE3OTliNiIsInZlcnNpb24iOjF9.WVE3fmYLkOW32_neYYj4TNJ5lhrG-27DnoJd4YDUzpHYvGWGoFU9CUuIFraQFnojRr02f3KqVY7T33DG5mpzBg
+    - type: rouge
       value: 23.6331
+      name: ROUGE-L
       verified: true
-    - name: ROUGE-LSUM
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTYyOTE0ODY2Mjk0YTk5ZTY5NTZkM2JkOGZhNjQ3NjNiMjVhNTc4ZmMwYzg1ZGIxOTA2MDQxNmU3Yjc5YWY0MSIsInZlcnNpb24iOjF9.yQ8WpdsyGKSuTG8MxHXqujEAYOIrt_hoUbuHc8HnS-GjS9xJ-rKO6pP6HYbi0LC9Xqh2_QPveCpNqr9ZQMGRCg
+    - type: rouge
       value: 28.7831
+      name: ROUGE-LSUM
       verified: true
-    - name: loss
-      type: loss
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzVkMDNlODA4NWI3OGI1OGFlNjFlNWE4YzY5ZDE1NDdhMjIwYjlkNDIxNDZjOGRiNTI1MGJkMmE0YWZiMDNhMiIsInZlcnNpb24iOjF9.qoxn2g70rbbX6sVCvm_cXzvYZf1UdTDU44vvEVdZL-4h36cJRCOx5--O1tZEVdyvlMVi-tYz1RSxLRwQd72FAw
+    - type: loss
       value: 2.903024673461914
+      name: loss
       verified: true
-    - name: gen_len
-      type: gen_len
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGM2M2NlY2Q3NjYxY2EyM2FkYmM5OGVhYzcyNjA3ZTFlYzc3M2M2ODNmNWVjNjZmMGNiODc4MWY5NWE2ZDMyNyIsInZlcnNpb24iOjF9.pC4UK75LbyVFFm0-fcStMtdQhbuHE37wkZHoVbSQOYSyxjI8yA46bQkPmgg5znby9FK_wIgGxC_4KOdEeN4jBw
+    - type: gen_len
       value: 60.7411
+      name: gen_len
       verified: true
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZWEwMDFiYjgyNzRhZDVmOWIzYzZlZWU5OTFkYmU4YzI2Mjk2OTg1ZDVlNzU0YzNhOWI1MmU2NTAxZWUzZmFlOCIsInZlcnNpb24iOjF9.Zepow4AFj1sQ6zyJGoy_Dl4ICKRtzZI2nVYWlTsDnGrBDT42ak9mFUuw-BjHR8dEVHJKmOZlLk6GJ09bL7tGAA
   - task:
       type: summarization
       name: Summarization
@@ -252,30 +275,36 @@ model-index:
       config: 3.0.0
       split: test
     metrics:
-    - name: ROUGE-1
-      type: rouge
+    - type: rouge
       value: 30.5046
+      name: ROUGE-1
       verified: true
-    - name: ROUGE-2
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYmVmN2NkZDE1ZDAzZjhiYWJkNWJjZDIwNGNkY2EzOTVlNzA3OTZlYmEyNDE5NzQwNmI4NTdmM2M3YzlmMGJiYSIsInZlcnNpb24iOjF9.UbgnlgTUEd2yhULHeNKHQaVtAYwE3CijYGZc5mZSZkwXGIwJxwkDimhyo6XxMr8iCsu_hQLEsEtN9CWTn0SrDw
+    - type: rouge
       value: 13.2577
+      name: ROUGE-2
       verified: true
-    - name: ROUGE-L
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzU5MTg1OGJkYzAwNmUwMDdmYTBhODBhYjkyOTdjODRjMDJiNjU0ZjkzYzYyYWJhZTA1YjQ2NTliNWUyYmY3YiIsInZlcnNpb24iOjF9.fuvr3vrY8CSYpSluLeMz9VnxysWSlFFRMnnR3ZKZOxlh7_UNwtlMMHWCH6Yfy65LzglLNsRSnWNrwn5OXP4vAw
+    - type: rouge
       value: 19.0306
+      name: ROUGE-L
       verified: true
-    - name: ROUGE-LSUM
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzMxYmY1ZmRlYzA4NzNkZDAzZjc0MzhkY2E4YTRiMmI0M2IwNDIwNzdiOWIzYTM1YTBmNWJkOWE3ODA3ZDM5NyIsInZlcnNpb24iOjF9.y-vzjHeER3iqyvSrjHUvy6Z_hom6aV0SRNV5CiB2efPmS7cL9nifoqpF2MJtip9RVn5nuuavlm-e3e2K0S5yDw
+    - type: rouge
       value: 28.3421
+      name: ROUGE-LSUM
       verified: true
-    - name: loss
-      type: loss
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNGJjYTdhMDIxYWVhYzM0MDRkYTM4MTI4YmRhOGEwYjE5OGU1NWNiYjIzOTdkM2FlNTUxNTJiNzBmNWQ1NDgyOSIsInZlcnNpb24iOjF9.32seuR1CHAtUR_UCCx1nTiv-u88ETqypzWt5iItexmFTlVkZjPw7whgM7KXtgJsPdWfdcClYif5Qpnbq-NycDA
+    - type: loss
       value: 3.9484164714813232
+      name: loss
       verified: true
-    - name: gen_len
-      type: gen_len
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTgyMzJhNjY1OTU4YmVmMzVlYmI1N2IzZTdiNzYwMTA4YzRlZjY1ZjRhN2IxNTE5NjhkYjA1ZmMzMzVhNDk5NyIsInZlcnNpb24iOjF9.Cn8b20pksnlZF8LaJPxKrmrPMDIJ4CAPfjGifB86RaA6pLSTyY_wYsqEb2JfAczViquk4HtV8MvLnv0cioLODQ
+    - type: gen_len
       value: 231.0762
+      name: gen_len
       verified: true
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTIxOTMyZDlhNjYwOTY5M2Q0ZTZiNWQ5YzAxMjE1OTllOGNhNWU4MjQ0OTBhZTE5NDZjNmEzZTZlOWRiMGY5NyIsInZlcnNpb24iOjF9.QK29Zwhj8jN3hWae54-qaF7vHdh1ijSa6Mq_4LdGcG8xHLCerVGg45H9B1XClCksMadp7auOzPa8CEjxYVpyBA
   - task:
       type: summarization
       name: Summarization
@@ -285,30 +314,36 @@ model-index:
       config: default
       split: test
     metrics:
-    - name: ROUGE-1
-      type: rouge
+    - type: rouge
       value: 36.8502
+      name: ROUGE-1
       verified: true
-    - name: ROUGE-2
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYmE2ZjI4YmJkZGVjZDkzNzU5ZmI2MDYzNGZkNjE2OGM0Y2Y0Nzk1NTc1ZmUyZmFhYjIwY2RhMDVkMzQ1MWIxYyIsInZlcnNpb24iOjF9.SZjhhFkKwvRrI-Yl29psn17u1RCISsmmLVXxo2kxCjkhtMOma-EzC5YidjPDGQLb-J2nvqUworaC2pL_oeHxDQ
+    - type: rouge
       value: 15.9147
+      name: ROUGE-2
       verified: true
-    - name: ROUGE-L
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODgwOTJhOWIyZDQ4ZDA5YWMzYTJkZWFmMzlkNWYxNTg5OGFiNzY0MTExNTgyMTdlMTQ1N2EwYWY4OGZkNWY5YyIsInZlcnNpb24iOjF9.DS-X3eA1tGhVSuUL8uSPtJMNijODF3ugaKEtBglmPqF1OQZwIwQs-NExNYP4d6Y4Pa9d-DujD5yfyl9C8HBGCw
+    - type: rouge
       value: 23.4762
+      name: ROUGE-L
       verified: true
-    - name: ROUGE-LSUM
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTYxNTA4YzhmYTQ0YmRjMWU5ZDliZWFhMjM4ZmUyNGUyOWJhNzA1MDBhZDliYmYyYzY3NjBmZTZlYWY3YTY3ZCIsInZlcnNpb24iOjF9.o0W7dqdz0sqMPKtJbXSRpyVNsREEUypW-bGv7TW5lfJFkijfDKhVITEClFLWu5n2tIV-sXAYxgQHDf5_hpY-Dw
+    - type: rouge
       value: 30.9597
+      name: ROUGE-LSUM
       verified: true
-    - name: loss
-      type: loss
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzEzOGNiYjk4NDkxNTFmMjA5YjM1YTQzZTk2N2JiZDgxNzAxYzFlYjliZjA3NmRjMzZlNGYyODBkNTI1NzVjNiIsInZlcnNpb24iOjF9.C_hobTR0ZY958oUZcGEKj2RoPOkyfMCTznwi4mUx-bfGRRAecMyn45bWVwwRq12glk1vThDetCjOMHA6jgSDCw
+    - type: loss
       value: 3.878790855407715
+      name: loss
       verified: true
-    - name: gen_len
-      type: gen_len
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNmYyOWM0YWQ0MjAxZDg5ZWQyNDk3MGUwNzdkOWIwZDc0OGJjYTU3YjZmOWY0YTljNDI0OWRlNTI0ZDMwZWEzOCIsInZlcnNpb24iOjF9.P01Jzfa-5jyMeoEqEsEluKOydNmtRtNy8YhwfJuYHVJTVDzCIfzY8b7iNfqTfKFKwKkZ4eTwmA6vmsPZeASDAw
+    - type: gen_len
       value: 131.3622
+      name: gen_len
       verified: true
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYmJjN2Q5ZGNlZjQ2ODJiYTZlMzZmNWVmMzRlMGQ0ZTkxZWM3ZDQ4ZmQ1NmUyZjY4MTVhZGE5NDFiZTBhNDZiYSIsInZlcnNpb24iOjF9.DqYNc0ZCX_EqRi4zbSBAtb-js_JBHSWZkeGR9gSwEkJletKYFxPGZWd-B1ez88aj6PO775-qHd98xx3IWCHECQ
   - task:
       type: summarization
       name: Summarization
@@ -318,30 +353,36 @@ model-index:
       config: y
       split: test
     metrics:
-    - name: ROUGE-1
-      type: rouge
+    - type: rouge
       value: 33.7585
+      name: ROUGE-1
       verified: true
-    - name: ROUGE-2
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2VmMGU5YWJlZWFlNjA3MDY2NTBmZWU3YWQxYTk3OGYzZmU5NmFmMTQ1NTVmNDQyZTJkNDMwY2E5NGRjMGU3MSIsInZlcnNpb24iOjF9.P6Rt9c3Xi_B-u8B1ug4paeZDoAO4ErGeNM0gELHGeOMj4XMjeSvyAW_-30cA9Wf23-0jGPOSZbN5pME4JpxfDA
+    - type: rouge
       value: 9.4101
+      name: ROUGE-2
       verified: true
-    - name: ROUGE-L
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDA0NzUxMjIwYTFjNGQ5YTA4YjE1NGU5YWMzYjhiOTk2NWE3ZGQxNDY4YTI3ZmI0ODBjYmJkZjcwYTM2OTg2MCIsInZlcnNpb24iOjF9.23hd2SuLoX3_Rygj2ykcSQccPeFsf4yLDAgvS189jx6JNln0MVR6YI2-3Yzo5g8LJk0MCbgkOp0my-nf7nMaDw
+    - type: rouge
       value: 18.8927
+      name: ROUGE-L
       verified: true
-    - name: ROUGE-LSUM
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODhhMGZiZWFlNmZkYmYxZjJmODE1NWRiZjI2OGU1MTc4MDkyYjk1Mzk5ODFkYWVhY2ExNTViYjJmYzkzNWJhYiIsInZlcnNpb24iOjF9.SkKhf-l2cl2KcuC17oPrBtkBlZJaj2ujCgzRlfZy76rU9JtlW7N9bcy1ugnw-vRVUVVR6wUK08T45YorfuxqBg
+    - type: rouge
       value: 28.5051
+      name: ROUGE-LSUM
       verified: true
-    - name: loss
-      type: loss
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTgzYzA0NmQ0OTZmNzJkNGZiNTdmMzFmOTljMWE3YzM0NDg2MDY1ZDY5ZTE4MmQ5YzU1ZDFiNmE2ZjkwMjRjMiIsInZlcnNpb24iOjF9.p1TQINRxMatNe77_BMnusSg1K5FOD9f1_N4TBJDjJHNhYnyQDE4pKHfK8j6fsHGg58DHVQjmm8g96SK4uMF6DA
+    - type: loss
       value: 5.162865161895752
+      name: loss
       verified: true
-    - name: gen_len
-      type: gen_len
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZWM1YTQ4MjVmMDkyZDI3OWJmODhmOWE2MDYyMDA4OGRmYzhiY2YzZjVmMTZkMTI4NjBlY2MwMDY3ZDE5ZjlmMyIsInZlcnNpb24iOjF9.Czh4TOG-QIqyc_-GJ3wc1TLuxc-KLwPelV5tiwEjNhZFyUZkjLH__ccOxBk9TYy2vunvh2AwdY3Mt6Fr8LhaDA
+    - type: gen_len
       value: 222.6626
+      name: gen_len
       verified: true
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiY2JjNzVkODhmOWQ5NWMwNDdlNzhkYjE5NjY3NTgwNWVmZDZlMzc4NDdmZjdlN2M2ODBkZGU5NGU0ZjMzM2Q5OCIsInZlcnNpb24iOjF9.z4hZ-uXg8PPn-THRHFrsWZpS3jgE8URk5yoLenwWtev5toTrZ2Y-DP8O30nPnzMkzA4yzo_NUKIACxoUdMqfCQ
   - task:
       type: summarization
       name: Summarization
@@ -351,30 +392,36 @@ model-index:
       config: default
       split: test
     metrics:
-    - name: ROUGE-1
-      type: rouge
+    - type: rouge
       value: 38.7332
+      name: ROUGE-1
       verified: true
-    - name: ROUGE-2
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGViMThhNTdlZDRiMTg5NTZjNGVmOThiMjI5NDEyZDMxYjU4MTU2ZTliZjZmMzAzMmRhNDIxYjViYjZmNWYwNSIsInZlcnNpb24iOjF9.SK_1Q9WlkNhu3mfsyir1l72pddjURZvJV3mcJ4jhBxS2k2q1NAR8JT_iT8v1thLiv8NUDmDr2o9Dig4A8svDBw
+    - type: rouge
       value: 11.0072
+      name: ROUGE-2
       verified: true
-    - name: ROUGE-L
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzkzMDU1ZGZlOWUwOGQyY2UwMWFjZTY1MDBmNzcyZGYzZTliNGVkNDZjZDVjZjA4NmE3OWVhMGIyZmE3NGE0NSIsInZlcnNpb24iOjF9.j0wvR0NPw0lqxW3ASbmBvxAbFHGikXw-Y7FjutojhzTfSs3BIs5Z8s5_h6eesvSGT5fS_qUrbnl9EEBwjrXqDg
+    - type: rouge
       value: 18.6018
+      name: ROUGE-L
       verified: true
-    - name: ROUGE-LSUM
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjIwNTUzN2ZhZjU5OGFhYzRmZmEwY2NkZWVjYmYzZjRjMGIxNzNjZDY5YzIyMTg2NDJkMGYxYmViNTcwOTc5NCIsInZlcnNpb24iOjF9.rD_tFYRyb-o6VX7Z52fULvP_HQjqqshqnvbjAxWjuCM9hCn1J6oh0zAASPw0k1lWiURbiMCiaxIHxe_5BN_rAQ
+    - type: rouge
       value: 34.5911
+      name: ROUGE-LSUM
       verified: true
-    - name: loss
-      type: loss
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiY2Q4MWY3NGFhNjE5YjE5NzIyODVhNTYxNWFmZDE5NjNiZTM1M2M3ZmIwNTZiOWEyMTc2MzQ0MWQ5YTdjYThlNyIsInZlcnNpb24iOjF9.R789HgYsv_k6OrjocVi0ywx0aCRlgOKpEWUiSUDca-AfoDS8ADJBtLYoEKg1wnRlR9yWoD4vtEWdKbyOOln1CA
+    - type: loss
       value: 3.5744354724884033
+      name: loss
       verified: true
-    - name: gen_len
-      type: gen_len
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzBjZTk0YWMwMzQxNDRlY2UxZDc4NTE1MmEzNDkwM2M3ZGZhNGMzNmI4ZDU2ZTVhZDkwMjNhYTkxZTIwN2E4MyIsInZlcnNpb24iOjF9.bDQ_3-CumosWKroMwBEMwKnDAj4ENQbUnbS387hU0zAY1K5g1NOy7fKBohxYZnRVolEfiuhszifUMW9zcLjqCA
+    - type: gen_len
       value: 192.0014
+      name: gen_len
       verified: true
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDQxZmEwYmU5MGI1ZWE5NTIyMmM1MTVlMjVjNTg4MDQyMjJhNGE5NDJhNmZiN2Y4ZDc4ZmExNjBkMjQzMjQxMyIsInZlcnNpb24iOjF9.o3WblPY-iL1vT66xPwyyi1VMPhI53qs9GJ5HsHGbglOALwZT4n2-6IRxRNcL2lLj9qUehWUKkhruUyDM5-4RBg
 ---
 
 # Longformer Encoder-Decoder (LED) for Narrative-Esque Long Text Summarization