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Add verifyToken field to verify evaluation results are produced by Hugging Face's automatic model evaluator (#2)
Browse files- Add verifyToken field to verify evaluation results are produced by Hugging Face's automatic model evaluator (b3dc64e9033fee10e40afc70931d8687a456c6ca)
Co-authored-by: Evaluation Bot <autoevaluator@users.noreply.huggingface.co>
README.md
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---
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language:
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- en
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tags:
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- summarization
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- pegasus
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license: apache-2.0
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datasets:
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- kmfoda/booksum
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metrics:
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deviation of the average recurrence interval, the more specific could be the long
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term prediction of a future mainshock.
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example_title: earthquakes
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- text:
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\ this function space (Section 5)."
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example_title: scientific paper
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- text: ' the big variety of data coming from diverse sources is one of the key properties
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of the big data phenomenon. It is, therefore, beneficial to understand how data
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in their business An important area of data analytics on the edge of corporate
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IT and the Internet is Web Analytics.'
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example_title: data science textbook
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- text:
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example_title: bigbird blog intro
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inference:
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parameters:
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config: kmfoda--booksum
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split: test
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metrics:
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type: rouge
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value: 29.1023
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verified: true
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value: 6.2441
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verified: true
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value: 14.7503
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verified: true
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value: 27.2375
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verified: true
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value: 2.979011058807373
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verified: true
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value: 467.269
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verified: true
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---
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---
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language:
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- en
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+
license: apache-2.0
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tags:
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- summarization
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- pegasus
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datasets:
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- kmfoda/booksum
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metrics:
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deviation of the average recurrence interval, the more specific could be the long
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term prediction of a future mainshock.
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example_title: earthquakes
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- text: ' A typical feed-forward neural field algorithm. Spatiotemporal coordinates
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are fed into a neural network that predicts values in the reconstructed domain.
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Then, this domain is mapped to the sensor domain where sensor measurements are
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available as supervision. Class and Section Problems Addressed Generalization
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(Section 2) Inverse problems, ill-posed problems, editability; symmetries. Hybrid
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Representations (Section 3) Computation & memory efficiency, representation capacity,
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editability: Forward Maps (Section 4) Inverse problems Network Architecture (Section
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5) Spectral bias, integration & derivatives. Manipulating Neural Fields (Section
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6) Edit ability, constraints, regularization. Table 2: The five classes of techniques
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in the neural field toolbox each addresses problems that arise in learning, inference,
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and control. (Section 3). We can supervise reconstruction via differentiable forward
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maps that transform Or project our domain (e.g, 3D reconstruction via 2D images;
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Section 4) With appropriate network architecture choices, we can overcome neural
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network spectral biases (blurriness) and efficiently compute derivatives and integrals
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(Section 5). Finally, we can manipulate neural fields to add constraints and regularizations,
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and to achieve editable representations (Section 6). Collectively, these classes
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constitute a ''toolbox'' of techniques to help solve problems with neural fields
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There are three components in a conditional neural field: (1) An encoder or inference
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function € that outputs the conditioning latent variable 2 given an observation
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0 E(0) =2. 2 is typically a low-dimensional vector, and is often referred to aS
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a latent code Or feature code_ (2) A mapping function 4 between Z and neural field
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parameters O: Y(z) = O; (3) The neural field itself $. The encoder € finds the
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most probable z given the observations O: argmaxz P(2/0). The decoder maximizes
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the inverse conditional probability to find the most probable 0 given Z: arg-
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max P(Olz). We discuss different encoding schemes with different optimality guarantees
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(Section 2.1.1), both global and local conditioning (Section 2.1.2), and different
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mapping functions Y (Section 2.1.3) 2. Generalization Suppose we wish to estimate
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a plausible 3D surface shape given a partial or noisy point cloud. We need a suitable
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prior over the sur- face in its reconstruction domain to generalize to the partial
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observations. A neural network expresses a prior via the function space of its
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architecture and parameters 0, and generalization is influenced by the inductive
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bias of this function space (Section 5).'
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example_title: scientific paper
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- text: ' the big variety of data coming from diverse sources is one of the key properties
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of the big data phenomenon. It is, therefore, beneficial to understand how data
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in their business An important area of data analytics on the edge of corporate
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IT and the Internet is Web Analytics.'
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example_title: data science textbook
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- text: 'Transformer-based models have shown to be very useful for many NLP tasks.
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However, a major limitation of transformers-based models is its O(n^2)O(n 2) time
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& memory complexity (where nn is sequence length). Hence, it''s computationally
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very expensive to apply transformer-based models on long sequences n > 512n>512.
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Several recent papers, e.g. Longformer, Performer, Reformer, Clustered attention
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try to remedy this problem by approximating the full attention matrix. You can
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checkout 🤗''s recent blog post in case you are unfamiliar with these models.
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BigBird (introduced in paper) is one of such recent models to address this issue.
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BigBird relies on block sparse attention instead of normal attention (i.e. BERT''s
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attention) and can handle sequences up to a length of 4096 at a much lower computational
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cost compared to BERT. It has achieved SOTA on various tasks involving very long
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sequences such as long documents summarization, question-answering with long contexts.
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BigBird RoBERTa-like model is now available in 🤗Transformers. The goal of this
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post is to give the reader an in-depth understanding of big bird implementation
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& ease one''s life in using BigBird with 🤗Transformers. But, before going into
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more depth, it is important to remember that the BigBird''s attention is an approximation
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of BERT''s full attention and therefore does not strive to be better than BERT''s
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full attention, but rather to be more efficient. It simply allows to apply transformer-based
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models to much longer sequences since BERT''s quadratic memory requirement quickly
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becomes unbearable. Simply put, if we would have ∞ compute & ∞ time, BERT''s attention
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would be preferred over block sparse attention (which we are going to discuss
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in this post).
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If you wonder why we need more compute when working with longer sequences, this
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blog post is just right for you!
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Some of the main questions one might have when working with standard BERT-like
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attention include:
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Do all tokens really have to attend to all other tokens? Why not compute attention
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only over important tokens? How to decide what tokens are important? How to attend
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to just a few tokens in a very efficient way? In this blog post, we will try to
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answer those questions.
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What tokens should be attended to? We will give a practical example of how attention
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works by considering the sentence ''BigBird is now available in HuggingFace for
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extractive question answering''. In BERT-like attention, every word would simply
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attend to all other tokens.
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Let''s think about a sensible choice of key tokens that a queried token actually
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only should attend to by writing some pseudo-code. Will will assume that the token
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available is queried and build a sensible list of key tokens to attend to.
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>>> # let''s consider following sentence as an example >>> example = [''BigBird'',
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''is'', ''now'', ''available'', ''in'', ''HuggingFace'', ''for'', ''extractive'',
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''question'', ''answering'']
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>>> # further let''s assume, we''re trying to understand the representation of
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''available'' i.e. >>> query_token = ''available'' >>> # We will initialize an
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empty `set` and fill up the tokens of our interest as we proceed in this section.
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>>> key_tokens = [] # => currently ''available'' token doesn''t have anything
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to attend Nearby tokens should be important because, in a sentence (sequence of
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words), the current word is highly dependent on neighboring past & future tokens.
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This intuition is the idea behind the concept of sliding attention.'
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example_title: bigbird blog intro
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inference:
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parameters:
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config: kmfoda--booksum
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split: test
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metrics:
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- type: rouge
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value: 29.1023
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name: ROUGE-1
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verified: true
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verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTFhNjg4YTFlODU5MmVjNGVmNDRmMjQ4M2YyZGNmMWRlYjBhZmVhMTY3ZTUxNDkzNjY0OGVmNWJlNmY1OTkzNCIsInZlcnNpb24iOjF9.E_rVKqB7WEerLeRq6JIVTLZ1TgmsThFQJVKh11WH1qWa-cL3766psPWDKe8mK3lNkjmwbiDW0DZlDt4dm2ATCA
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+
- type: rouge
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value: 6.2441
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+
name: ROUGE-2
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verified: true
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+
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDVmZmFlOTgwN2Q3ZWRkZGVkMzU1ZDRkYzU1MWMzMTk1NDM5YTU0MzFjNDljNmZlY2I2NjZmZjcyYjBkZGExZCIsInZlcnNpb24iOjF9.QnuGoMWX8cq5_ukRtiaLRLau_F9XiCjg313GC7Iu1VGK8Kj_9lzU43377VsH0fBWooA1zJjtIK0UA-YpGQQOAA
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+
- type: rouge
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value: 14.7503
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+
name: ROUGE-L
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verified: true
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+
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzJhNzE0YjZiZWQ4NDE1Yjg3ZGJjY2ZmYWEwYzU5MTRhYWNiNTcyODU1NzM5NTZhNjNlNmYwNDVlYmZmYjkxOCIsInZlcnNpb24iOjF9.m5BLUMefXa1KivIIE9-gYKYq5aRRbfpQWazqzXxfCsqqp38Lt0ymk6OwXSlQyB_5oksNHIDFKpJX4wjYx2i7Bw
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+
- type: rouge
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value: 27.2375
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+
name: ROUGE-LSUM
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verified: true
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+
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTY1OTIxMzBkMGJiZmNiNjZjYmQ2MjUwMjBkYTg5Zjc1NjVlZjllNTg0MDM1NTdhZDJlZmIwOTczOGNkZDc5YyIsInZlcnNpb24iOjF9.bThI16mvqhEuGBhdao0w8j03vv9G9Quy-ITRZzalr41zOour9it4oxEPFCvmPf-nLCQkqgWKUDEzgr6Ww8qgBg
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+
- type: loss
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value: 2.979011058807373
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+
name: loss
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verified: true
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+
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOGM0NzM3YTI4Njg4NDY0ZjQzNTZmYTIxYzcxNDBlNzAwNTAxNDE4MTZjYmZmNzYwODU0OWQ1ZjM5YjRmMmFkZiIsInZlcnNpb24iOjF9.EPEP53AoqHz0rjVGStJI2dM7ivxFmOj572I3llWdAoejm3zO1Iq5WDArYsqOse_oLxYCgcqPmNVc5IcLW9x7Dg
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+
- type: gen_len
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value: 467.269
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+
name: gen_len
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verified: true
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+
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjgzYzU2ZjkwN2RhNzJlZmQyZTBlYmUxMTZhNzg0ODMwMjA3OTUzNTIwOWFkZWVmNjVmMTJiZmZhNWFmY2UzZCIsInZlcnNpb24iOjF9.RW5tzk2fcc_m4bgaSopRDFhSR9R8hRaYKrstXH4X5iGP_Xwvhy5Q7-igd2ACnlxIfmtdTmMxLMsvHr5oAZEwDg
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---
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