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

README.md

@@ -1,6 +1,9 @@
 ---
 language:
 - en
+license:
+- apache-2.0
+- bsd-3-clause
 tags:
 - summarization
 - led
@@ -9,9 +12,6 @@ tags:
 - booksum
 - long-document
 - long-form
-license:
-- apache-2.0
-- bsd-3-clause
 datasets:
 - kmfoda/booksum
 metrics:
@@ -30,39 +30,38 @@ 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
@@ -107,50 +106,62 @@ 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
@@ -188,30 +199,36 @@ model-index:
       config: kmfoda--booksum
       split: test
     metrics:
-    - name: ROUGE-1
-      type: rouge
+    - type: rouge
       value: 31.7308
+      name: ROUGE-1
       verified: true
-    - name: ROUGE-2
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjJmZjMxYTY0OGU3MzNjNmIzNmYyODNlNDg2ZGRhZDAzNTMwMDM5YWMxODc1OTc1ZWE3MzM2OTg1ODFhZDBkNCIsInZlcnNpb24iOjF9.B8BCKgySYVZW910_1zP0LfCpQYJbAe6loyWut76JlgZb2kV1_x9ybqtNESX0ka-lNqhYyXUNDpuS-7pTmsJVDg
+    - type: rouge
       value: 5.3311
+      name: ROUGE-2
       verified: true
-    - name: ROUGE-L
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzViMmY4ODFjYTc5ODk5MmRhMDQ3ZDRiYWQwMDg0OTk3ZTA4NDAxYTNiNDgyMmI4NDA3ZDMwYWViOTBkODBjNyIsInZlcnNpb24iOjF9.MOhJLDcgvv93mVFL1igIgIiTAH3b2Xa4gmBObq7RF44Mmu8Kxtd1KP7rOlDVFOrtrsooGPGsyE1GMCQ2kqeMDg
+    - type: rouge
       value: 16.1465
+      name: ROUGE-L
       verified: true
-    - name: ROUGE-LSUM
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzNjMzEwMTliZGE3ZmQ4M2UxMDAyMTY3YzJjZmMyMDYyN2YyNDM0N2VhNzI1MDc1YTg4MTRjMmEzNjVkNTk1NCIsInZlcnNpb24iOjF9.XLJ-DVKiYLlbw5E5rWADKbzUzf5fNHhlTCWPCC5dU4NI9Yeh76aR7TPt36ZzLDwTBknnR8KHqlaF8F8YAvBUAg
+    - type: rouge
       value: 29.0883
+      name: ROUGE-LSUM
       verified: true
-    - name: loss
-      type: loss
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTcwNzEwMmE5NjQxZTkzYmQyZDZmNzllYzYyNGI5OTMyNWMwNjdiM2I2YmM5YjdmY2E5OWQ3OTk3ZDA1MTc3YyIsInZlcnNpb24iOjF9.d6rFxjCB6RJNI_pn2DNNSjuZe4rdvj0RatkaTJRp5lP0F_AFfU5Zn9zRWzZJV7V-xMauIc4UhfdoLp9r_-CABA
+    - type: loss
       value: 4.815707206726074
+      name: loss
       verified: true
-    - name: gen_len
-      type: gen_len
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTMwMTgxMmJkODY3MjkzOWJhMzJhOTIxMWVkODhjZmM0MWUzMWQ1N2JkZjRhOTQxNmU1YWVjYzQ0MDNlZWI3OSIsInZlcnNpb24iOjF9.mkBQHYhYFfDV6F4klXGJ1dSsF-pbCs-6F9zcw6IYznwmXUjtk7m5J4Zt4JAju5LKz4YizvEcUCl_L0WddnfvDA
+    - type: gen_len
       value: 154.9036
+      name: gen_len
       verified: true
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTc0ZmM1ZDM4MDE0MzY3MDM3OWJhNDkzZjJkZDdkMjU5M2JmMDJjYTIxODA1OTllNmY5ZWQzZDlmNWFiYzk4NiIsInZlcnNpb24iOjF9.VQ_O_xSTz870tnM08PJXQOwg9OsNNwI_HVX4S7AuW57_FzGGyRaWSuGE5SWzRS4Tur9YP0QxV4VV0Yoaoi3IAA
   - task:
       type: summarization
       name: Summarization
@@ -221,30 +238,36 @@ model-index:
       config: samsum
       split: test
     metrics:
-    - name: ROUGE-1
-      type: rouge
+    - type: rouge
       value: 33.4484
+      name: ROUGE-1
       verified: true
-    - name: ROUGE-2
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTk4Yjg1YTc4YmY0MzBiZDU4ZjFhNzI4MjZkMWU1MzBlOWNlMjQ5ODMzY2YzYzRhYjJkMGUzNmI3ZjdkMzIzZSIsInZlcnNpb24iOjF9.AqS8A1OUiM0IZFBEGirv5F3Novk8lSUYSfPc3bYWLA6t-W7wgup3qA207eGbE5j9CkDWZ7QrSG1U6Z9A0sOqAA
+    - type: rouge
       value: 10.4249
+      name: ROUGE-2
       verified: true
-    - name: ROUGE-L
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiN2U4NjUyNTFmOGM5OTlhZDMyMTlmM2E4OWI2NGFiMDAyMGJjMzRjNWNlMGEyYWFmNTE5ZWMxM2I0ZGZmNWNmOCIsInZlcnNpb24iOjF9.SgJcHJ4qoRWXFvFiwv1PUutWktvsxQNynVPEv-GtBgxd6WI7o561ONyco5U-5tcyE_1SbSCJzz-L-R-q3cvoDA
+    - type: rouge
       value: 24.5802
+      name: ROUGE-L
       verified: true
-    - name: ROUGE-LSUM
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZmQ5MDI5MzdiNGE5NDM0MmU5OThmZTBkNjkxMzg5N2IxNGVlODdhZTZhNjg3NzFjYWEyMzA3MTQxNjMyMjRkOCIsInZlcnNpb24iOjF9.Bg5dHqCcJjmxa-xGWNR5lD9g3quX7lKkH0pjiTd2xE5WiPoLLN2c0mYa2GovdW7__WnYwhhHC7es03jmvyZbCw
+    - type: rouge
       value: 29.8226
+      name: ROUGE-LSUM
       verified: true
-    - name: loss
-      type: loss
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNGFhOTEwNGM1MmZkNDk2ZjQ1Y2MyNjM3MGI5MGY3MWVkM2I0MjU2NWFiYmEwMjE4MTJlZWIwOGQ2MjQ3YjgzYSIsInZlcnNpb24iOjF9.W_aQKs10oXQdKEczJBGM3iiwJgb-VaXTpyA3sGof5WbhHf9vITAQA-xvynh5LgKtXQ1zjx737hnHgjEsu_Y0Cw
+    - type: loss
       value: 4.176078796386719
+      name: loss
       verified: true
-    - name: gen_len
-      type: gen_len
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiN2JhODQ5YTZkNDZkZGYyNGU2MzkxMWU5MTEwMGM2YmVjZTA5YzI5NTMxMDNhYjhlOTAxMzFiMDYwYmM0MjEzZCIsInZlcnNpb24iOjF9.OvZrPBOR5jhkoTGBgsInkH7j3_xpacXHDoT7UIXEnyXzadfBO-O-K6fjalLNZw8wSkbjHIFcL_6S_qTTxPsNAQ
+    - type: gen_len
       value: 65.4005
+      name: gen_len
       verified: true
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2NhYjc3ZjQzNDEwYmMzOTM0ODkyZTJhZWNhNzZhYmEyZTYxMzA2YTYzMWFjOTA5ZjlhYWMzODg3NzY1ZTUwYSIsInZlcnNpb24iOjF9.vk9bgmtQFeRwdY3VXjtrJr_5wUCIeoAkI3kO0cHxhxmJo6RvUnyXiut72FuB-mlLZvqgiNkaZ-u_bh0Z3DjuCw
   - task:
       type: summarization
       name: Summarization
@@ -254,30 +277,36 @@ model-index:
       config: default
       split: test
     metrics:
-    - name: ROUGE-1
-      type: rouge
+    - type: rouge
       value: 40.5843
+      name: ROUGE-1
       verified: true
-    - name: ROUGE-2
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTVjMDkyMWZjYTQ0NzgzNGUxZjNiMTg3NjU1MWJlNTQ2MWQ1NjE1MDk1OTU4ZjJiNGQ5ODg3Y2VlMWUyMzllNyIsInZlcnNpb24iOjF9.OhqBcVIuHk7fzmdrsWMvUe1bLeVMZVstZUoZpP7C1vR-3aIDl7r6eBmPrt5w-KcNq5p4teNPBsq7oKzbd5ZgDQ
+    - type: rouge
       value: 17.3401
+      name: ROUGE-2
       verified: true
-    - name: ROUGE-L
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNGQxYmQzMmE0OTcyNTM5NmMwNjIxNzYxZDcwMDFkYzJkOWY4YWY3NTdhZGRhZDdlMDAxNzcwODQ5OGM3Mzc1MCIsInZlcnNpb24iOjF9.Pksn25EEqvmx757N7Swrd4yXc_xU7-AMN9yNe8lrbBa-l1LoI_2PUASvnjML4f705cfuyMAfb0FkFp5WfER2AA
+    - type: rouge
       value: 25.1256
+      name: ROUGE-L
       verified: true
-    - name: ROUGE-LSUM
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjhjYzI5MDBiMjk2NTY3MDNmZTdiOGYwMTRlYjIwZjAwMjdlNTAyYzdhYTJlODQ4MjYzYmQ3MjRlYTA2YzhhZSIsInZlcnNpb24iOjF9.1jPepsweS2bzIqDverQzzhmhFGch7gpoEGFGqQ8zW7K10aUKWFX8lt-uZAmTa1Z5ZhzyXGBzc3dReFPhWRRJBg
+    - type: rouge
       value: 34.6619
+      name: ROUGE-LSUM
       verified: true
-    - name: loss
-      type: loss
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2VkZDIxNWJjOTA0NzFjOTIwOTdjYjc1M2EyNDVjZjY2ZjY3MjIxNDk3YTc5YWExNzAwN2FhOTc1NjVhYjBkYiIsInZlcnNpb24iOjF9.8opqHSUckPohoSF9jfPTpXDz2AtDwvdMqOdIXx2kE1tkOcbLPbOBfcc8RhRR98y8S26yC6EYFhFnf03CV2ejAQ
+    - type: loss
       value: 4.792657375335693
+      name: loss
       verified: true
-    - name: gen_len
-      type: gen_len
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTY5ZTRkMGU3OGVkODMzMDU5OWE1NTM5YjA4NDliZDlmNzc2NzZjNjFmNTA3M2EwY2NmN2E0MWJmZjQ5ZDliMiIsInZlcnNpb24iOjF9.KCKdk8xt2NWcMmYKV3-9eVEsFm9MqGllSMu9QCFJFIQlnyNXllHKdBLouoaGQz8IRYXvZKH8_TLDPIQx-31jAg
+    - type: gen_len
       value: 163.9394
+      name: gen_len
       verified: true
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzdkZDYyZGUzYmFkZmI2NjUwYmQ0MzZjMmIyZjI1YTFiMzM4OThiZjBiMzljOTVkZTgwMjA0NTE5OGM2YmFjMiIsInZlcnNpb24iOjF9.XyMZLUdkUIF32KTJMuv_bJswQCx_Tfg4Fx823cURUixSeoIKps8_a634AreZ3Z8kb7bfE_sFGh3rM9KWsMxlDw
   - task:
       type: summarization
       name: Summarization
@@ -287,30 +316,36 @@ model-index:
       config: default
       split: test
     metrics:
-    - name: ROUGE-1
-      type: rouge
+    - type: rouge
       value: 39.0834
+      name: ROUGE-1
       verified: true
-    - name: ROUGE-2
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjYzMmVlMDM4MTNkMTI4MjAyMTU2YTg1ZWQwNTI1MmJlNGUwZmE1NTRmYTljZTQwY2RlMjcxOTgyZGMyYTc0ZiIsInZlcnNpb24iOjF9.6yuSr7UmsFatwqQ-mEO4gmsEtWI05kGB5Ib2pnl05H1OiPT2uUwmqdUytUw8KTx9u1jv9q0cTF1cL-n2kPEJAA
+    - type: rouge
       value: 11.4043
+      name: ROUGE-2
       verified: true
-    - name: ROUGE-L
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWI5N2U2ZWI1ODM2MWUwOTIzYTAzNmRhNDA2OWEzZWRjMGEzMjBmY2EwN2YyYzU1NWE0YjIyZDE3MWE0MmMxZCIsInZlcnNpb24iOjF9.wonuxbBl25TzEaHUH_E816nHJ1OSXKfkaq7eJzbLpsfeGwcDklxUSxZxRO7VBiBMaY3Qttf9ywmEIPp40HnpBA
+    - type: rouge
       value: 19.1813
+      name: ROUGE-L
       verified: true
-    - name: ROUGE-LSUM
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZjU1NDZhN2NkMzZiZGJkODE4NDZiYjViOTZkNGMyNDlkNjBlZmFjYzU1N2IzMjFjYjY1MDU1Zjk2MzA0M2U4NyIsInZlcnNpb24iOjF9.bTCRzv3J9NiCh4aV23tAWGTvrdQCv_RS40zGwC4AJXtGS40cY7tJHYwBf9U9_rCetDBxqfjJpdaUbCAOglxLAA
+    - type: rouge
       value: 35.1581
+      name: ROUGE-LSUM
       verified: true
-    - name: loss
-      type: loss
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDNhNTUyZjE4NjYxYjIzYThmMDM2YWNhM2QwYzY1ODI2ZTE3NmNjMmVhOTAzZjZlOWQwYzc1NzU2NDNjNzIxMyIsInZlcnNpb24iOjF9.cWlSbEBgrMN5D-fV_yL9geNMyMkIItcVO3wehNJPzFi3E0v1-4q8pnX-UgjLzto8X7JLi6as2V_HtZE4-C-CDw
+    - type: loss
       value: 4.654905319213867
+      name: loss
       verified: true
-    - name: gen_len
-      type: gen_len
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTc5Nzk0ODhiNWUzNTAxNzk2YzZmMjU2NDliY2UzOTYyYTdmZGEyYjI5NDNhOTE0MGUxOTgxMGVjMmNhM2UyMSIsInZlcnNpb24iOjF9.eBBAebcl3AwkrjR6a8BvoSjDfpw8LWTRFjyIFHVzspvoOKVfnO8_NB_UeR_K127OwXyoZ70Z7X_aKJOe-2kTDA
+    - type: gen_len
       value: 186.2494
+      name: gen_len
       verified: true
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOWI2NjVlYjgwYWJiMjcyMDUzMzEwNDNjZTMxMDM0MjAzMzk1ZmIwY2Q1ZDQ2Y2M5NDBlMDEzYzFkNWEyNzJmNiIsInZlcnNpb24iOjF9.iZ1Iy7FuWL4GH7LS5EylVj5eZRC3L2ZsbYQapAkMNzR_VXPoMGvoM69Hp-kU7gW55tmz2V4Qxhvoz9cM8fciBA
 ---
 
 # Longformer Encoder-Decoder (LED) for Narrative-Esque Long Text Summarization