How to Use Facebook Zero on Any Device: A Guide to the Data-Saving Version of Facebook
Yin et al. proposed a method for using pre-trained NLI models as a ready-made zero-shot sequence classifiers. The method works by posing the sequence to be classified as the NLI premise and to construct a hypothesis from each candidate label. For example, if we want to evaluate whether a sequence belongs to the class "politics", we could construct a hypothesis of This text is about politics.. The probabilities for entailment and contradiction are then converted to label probabilities.
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This method is surprisingly effective in many cases, particularly when used with larger pre-trained models like BART and Roberta. See this blog post for a more expansive introduction to this and other zero shot methods, and see the code snippets below for examples of using this model for zero-shot classification both with Hugging Face's built-in pipeline and with native Transformers/PyTorch code.
Facebook Zero is an initiative undertaken by social networking service company Facebook in collaboration with mobile phone-based Internet providers, whereby the providers waive data (bandwidth) charges (also known as zero-rate) for accessing Facebook on phones via a stripped-down text-only version of its mobile website (as opposed to the ordinary mobile website m.facebook.com that also loads pictures). The stripped-down version is available online only through providers who have entered the agreement with Facebook. Photos are not loaded by default. Users may still choose to view them by clicking through but regular data charges apply to photo use.
Plans for Facebook Zero were first announced at the Mobile World Congress in February 2010 by Chamath Palihapitiya. In collaboration with 50 mobile operators around the world, it was officially of launched on May 18, 2010. The scheme is considered zero-rated or the practice of offering free data for some services, filtering out others.
The Subsecretaría de Telecomunicaciones of Chile ruled that zero-rating services like Wikipedia Zero, Facebook Zero, and Google Free Zone, that subsidize mobile data usage, violate net neutrality laws and had to end the practise by June 1, 2014.
Fast-growing DTC brands implementing this playbook, and personalizing the customer journey with zero-party data, have connected with 16x more customers on email and SMS, seen 6x sales conversions, and increased revenue by over 50%.
"The writing is on the wall. Your success will be determined by how much you do right now. My best recommendation is to lean heavy into collecting zero and first party data (with an emphasis on zero-party data).The more data and insight you collect now while things are still somewhat accessible, the better you'll be in the long run. If you wait, it'll likely be too late."
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Founded in 2016, we are the pioneer zero-party data marketing platform proud to be powering thousands of ecommerce merchants (including Shopify themselves) to create real relationships with more site visitors and unlock higher conversion rates through personalization across marketing channels.
This practice, known as zero rating, is common, but it has also been controversial. Metered billing (selling network access based on data consumption) forces people to ration their use of the internet to avoid running out of data or incurring additional fees. Zero rating, in which some network traffic is not counted toward data limits, offers some relief from the constraint of data caps. However, many programs are structured so as to favor a few web offerings over all others, which undermines an open internet by leveraging the price of data to advantage the zero-rated sites.
Zero rating became a hot topic in 2015 as a different Facebook product, Free Basics, received intense scrutiny from policymakers and regulators. Mobile carriers around the world partner with Facebook to offer zero rated access to Free Basics, which functions as a portal to a selection of websites. One of the biggest critiques of Free Basics was the limited content available through the portal; it did not allow users to connect to the full internet, only a walled garden of sites that met the technical criteria and passed the approval process.
While a basic ZTP approach only applies configuration and upgrades software on a network device, we took a unique, more robust, and flexible approach: downloading a special piece of Python code in ZTP to be executed by the network device as our on-box agent. Once the ZTP process kicks off the Facebook Python agent on the network device, the agent performs several functions:
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Near-term targets outline how organizations will reduce their emissions, usually over the next 5-10 years. These targets galvanize the action required for significant emissions reductions to be achieved by 2030. Near-term targets are also a requirement for companies wishing to set net-zero targets.
Near-term targets outline how organizations will reduce their emissions over the next 5-10 years. These targets galvanize the action required for significant emissions reductions to be achieved by 2030. These reductions are critical to not exceed the global emissions budget. Near-term targets are also a prerequisite for companies wishing to set net-zero targets.
Net-zero targets encompass both near and long-term targets. Companies wishing to set net-zero targets under the Corporate Net-Zero Standard have both near- and long-term targets validated by the SBTi.
Hello @joeddav , please how can i train zero-shot classification pipeline on my own dataset because i get errors in classification of some texts , so i want to train this pipeline on my data set , thank you
Net Zero primarily means reducing greenhouse gas emissions caused by humans as close as possible to zero, with remaining emissions balanced by an equivalent amount of carbon removal from the atmosphere. While the main greenhouse gas is carbon dioxide, which makes work towards Net Zero carbon the central task, the Index also includes emissions of methane and nitrous oxide.
Hence, zero-shot text classification is about categorizing a given piece of text to some pre-defined group or class label without explicitly training a dedicated machine learning model on a downstream dataset containing text and label mapping.
In this blog, we will go through a quick tutorial on playing around with the zero-shot text classification pipeline from Hugging Face ? and also discuss what goes under the hood of the algorithm that makes it possible.
Hugging Face provides the concept of pipelines that make it really easy to infer from already trained models by abstracting most of the complex code. We will be using the same idea for the task of "zero-shot-classification". The Pipeline class is the base class from which all task-specific pipelines inherit. Hence, defining the task in the pipeline triggers a task-specific child pipeline, in this case, it would be ZeroShotClassificationPipeline. There are many other tasks which you can explore, and it is worth spending some time on seeing the entire list of tasks at Hugging Face Pipeline Tasks.
Nice! With the examples discussed above, it's pretty evident that this problem formulation can generalize to various downstream tasks. You can now go ahead and play around building other zero-shot use-cases. Also, feel free to check out this online demo. Now, let's move forward and delve into little details.