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

@@ -1,6 +1,7 @@
 ---
 language:
 - en
+license: apache-2.0
 tags:
 - summarization
 - summarisation
@@ -9,7 +10,6 @@ tags:
 - bigbird_pegasus_
 - pegasus
 - bigbird
-license: apache-2.0
 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
 inference:
   parameters:
@@ -171,30 +182,36 @@ model-index:
       config: kmfoda--booksum
       split: test
     metrics:
-    - name: ROUGE-1
-      type: rouge
+    - type: rouge
       value: 34.0757
+      name: ROUGE-1
       verified: true
-    - name: ROUGE-2
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzk3NmI2ODg0MDM3MzY3ZjMyYzhmNTYyZjBmNTJlM2M3MjZjMzI0YzMxNmRmODhhMzI2MDMzMzMzMmJhMGIyMCIsInZlcnNpb24iOjF9.gM1ClaQdlrDE9q3CGF164WhhlTpg8Ym1cpvN1RARK8FGKDSR37EWmgdg-PSSHgB_l9NuvZ3BgoC7hKxfpcnKCQ
+    - type: rouge
       value: 5.9177
+      name: ROUGE-2
       verified: true
-    - name: ROUGE-L
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzdmMGU5ODhiMjcxZTJjODk3ZWI3NjY0NWJkMDFjYWI1ZDIyN2YwMDBjODE2ODQzY2I4ZTA1NWI0MTZiZGQwYSIsInZlcnNpb24iOjF9.ZkX-5RfN9cR1y56TUJWFtMRkHRRIzh9bEApa08ClR1ybgHvsnTjhSnNaNSjpXBR4jOVV9075qV38MJpqO8U8Bg
+    - type: rouge
       value: 16.3874
+      name: ROUGE-L
       verified: true
-    - name: ROUGE-LSUM
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWU4ODExMjEwZjcyOWQ3NGJkYzM4NDgyMGQ2YzM5OThkNWIyMmVhMDNkNjA5OGRkM2UyMDE1MGIxZGVhMjUzZSIsInZlcnNpb24iOjF9.2pDo80GWdIAeyWZ4js7PAf_tJCsRceZTX0MoBINGsdjFBI864C1MkgB1s8aJx5Q47oZMkeFoFoAu0Vs21KF4Cg
+    - type: rouge
       value: 31.6118
+      name: ROUGE-LSUM
       verified: true
-    - name: loss
-      type: loss
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjY2ODJiZDg2MzI3N2M5NTU5YzIyZmQ0NzkwM2NlY2U0ZDQ5OTM0NmM5ZmI5NjUxYjA3N2IwYWViOTkxN2MxZCIsInZlcnNpb24iOjF9.9c6Spmci31HdkfXUqKyju1X-Z9HOHSSnZNgC4JDyN6csLaDWkyVwWs5xWvC0mvEnaEnigmkSX1Uy3i355ELmBw
+    - type: loss
       value: 3.522040605545044
+      name: loss
       verified: true
-    - name: gen_len
-      type: gen_len
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODAyZTFiMjUzYTIzNWI0YjQxOWNlZjdkYjcxNDY3ZjMyNTg3ZDdkOTg3YmEzMjFiYzk2NTM4ZTExZjJiZmI3MCIsInZlcnNpb24iOjF9.n-L_DOkTlkbipJWIQQA-cQqeWJ9Q_b1d2zm7RhLxSpjzXegFxJgkC25hTEhqvanGYZwzahn950ikyyxa4JevAw
+    - type: gen_len
       value: 254.3676
+      name: gen_len
       verified: true
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzdlY2U1ZTgwNGUyNGM4ZGJlNDNlY2RjOWViYmFkOWE0ZjMzYTU0ZTg2NTlkN2EyMTYyMjE0NjcwOTU4NzY2NiIsInZlcnNpb24iOjF9.YnwkkcCRnZWbh48BX0fktufQk5pb0qfQvjNrIbARYx7w0PTd-6Fjn6FKwCJ1MOfyeZDI1sd6xckm_Wt8XsReAg
   - task:
       type: summarization
       name: Summarization
@@ -204,30 +221,36 @@ model-index:
       config: plain_text
       split: test
     metrics:
-    - name: ROUGE-1
-      type: rouge
+    - type: rouge
       value: 40.015
+      name: ROUGE-1
       verified: true
-    - name: ROUGE-2
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzE1MGM3ZDYzMDgwZGRlZDRkYmFmZGI4ODg0N2NhMGUyYmU1YmI5Njg0MzMxNzAxZGUxYjc3NTZjYjMwZDhmOCIsInZlcnNpb24iOjF9.7-SojdX5JiNAK31FpAHfkic0S2iziZiYWHCTnb4VTjsDnrDP3xfow1BWsC1N9aNAN_Pi-7FDh_BhDMp89csoCQ
+    - type: rouge
       value: 10.7406
+      name: ROUGE-2
       verified: true
-    - name: ROUGE-L
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZjEwOTRjOTA4N2E0OGQ3OGY0OThjNjlkN2VlZDBlNTI4OGYxNDFiN2YxYTI2YjBjOTJhYWJiNGE1NzcyOWE5YyIsInZlcnNpb24iOjF9.SrMCtxOkMabMELFr5_yqG52zTKGk81oqnqczrovgsko1bGhqpR-83nE7dc8oZ_tmTsbTUF3i7cQ3Eb_8EvPhDg
+    - type: rouge
       value: 20.1344
+      name: ROUGE-L
       verified: true
-    - name: ROUGE-LSUM
-      type: rouge
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzkxZmJkYzdmOGI3Yzc1ZDliNGY3ZjE5OWFiYmFmMTU4ZWU2ZDUyNzE0YmY3MmUyMTQyNjkyMTMwYTM2OWU2ZSIsInZlcnNpb24iOjF9.FPX3HynlHurNYlgK1jjocJHZIZ2t8OLFS_qN8skIwbzw1mGb8ST3tVebE9qeXZWY9TbNfWsGERShJH1giw2qDw
+    - type: rouge
       value: 36.7743
+      name: ROUGE-LSUM
       verified: true
-    - name: loss
-      type: loss
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjgxNmQ1MmEwY2VlYTAzMTVhMDBlODFjMDNlMjA4NjRiOTNkNjkxZWNiNDg4ODM1NWUwNjk1ODFkMzI3YmM5ZCIsInZlcnNpb24iOjF9.uK7C2bGmOGEWzc8D2Av_WYSqn2epqqiXXq2ybJmoHAT8GYc80jpEGTKjyhjf00lCLw-kOxeSG5Qpr_JihR5kAg
+    - type: loss
       value: 3.8273396492004395
+      name: loss
       verified: true
-    - name: gen_len
-      type: gen_len
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzI4OTcwOGYzYmM5MmM2NmViNjc4MTkyYzJlYjAwODM4ODRmZTAyZTVmMjJlY2JiYjY0YjA5OWY4NDhjOWQ0ZiIsInZlcnNpb24iOjF9.p46FdAgmW5t3KtP4kBhcoVynTQJj1abV4LqM6MQ-o--c46yMlafmtA4mgMEqsJK_CZl7Iv5SSP_n8GiVMpgmAQ
+    - type: gen_len
       value: 228.1285
+      name: gen_len
       verified: true
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODY2OGUzNDlhNzM5NzBiMmNmMDZiNjNkNDI0MDkxMzNkZDE4ZjU4OWM1NGQ5Yjk3ZjgzZjk2MDk0NWI0NGI4YiIsInZlcnNpb24iOjF9.Jb61P9-a31VBbwdOD-8ahNgf5Tpln0vjxd4uQtR7vxGu0Ovfa1T9Y8rKXBApTSigrmqBjRdsLfoAU7LqLiL6Cg
 ---