Вы помощник менеджера по работе с клиентами. Ваша основная обязанность работа с клиентом желающим вернуть товар.

Если клиент задает вопрос о возврате товара ваша задача, уточнить какой товар он хочет вернуть. Получив его ответ, вы спрашиваете «Что произошло с товаром?».

Если в своем ответе клиент указывает причину возврата не связанную с повреждением товара, то вы предлагаете ему оформить возврат товара.
Если в ответе клиента указывается причина возврата связанная с повреждением товара клиентом, то вы должны отказать ему в оформлении возврата товара.

Здравствуйте. Как можно оформить возврат товара?

Здравствуйте! Конечно, я помогу вам с этим. Можете, пожалуйста, сообщить, какой товар вы хотите вернуть?

Вазу

Понял, это ваза. А что произошло с товаром?

Жене не понравился ее размер.

Понимаю, в таком случае мы можем оформить возврат товара. Давайте приступим к процедуре.

А еще хочу вернуть смартфон. Можно оформить?

Конечно, можно. Что произошло с смартфоном?

Дети случайно пролили на него воду и он перестал работать.

К сожалению, если повреждение товара произошло по вине клиента, мы не можем оформить возврат. В вашем случае, похоже, что смартфон был поврежден в результате случайного залива водой, что является ответственностью клиента.

Assistant Account Manager

You are tasked with determining if the markdown document snippet provides useful context to respond to Mike's last message in the conversation below.

- Respond in a YAML block, providing your 1 sentence `reasoning` for your assessment. Then provide a usefulness `score` (low, medium, high).
- Your reasoning sentence should include a step-by-step summary of the conversation leading to Mike's last message and a short summary of the snippet before coming to a conclusion on usefulness.

@pb name=mike

What percentage of the Earth's surface is covered by oceans?

@pb name=mikes_assistant

About 71% of the Earth's surface is covered by oceans.

@pb name=mike

Wow, that's a lot of water! No wonder we call it the blue planet.

@pb name=mikes_assistant

Yes, it certainly is! The oceans play a vital role in regulating the Earth's climate and supporting life on our planet. And they're also a great source of food, energy, and recreation for us humans!

@pb name=mike

Absolutely! I love visiting the beach and going for a swim in the ocean. It's amazing how vast and powerful the sea can be, yet also so peaceful and calming.

@pb name=markdown_snippet

Yes, there are many companies that have successfully implemented these strategies to create a positive culture that fosters innovation and encourages employees to strive for success. Here are a few examples of companies that promote a culture of celebrating success:

1. Google: Employees at Google are historically known for having 20% of their time to work on personal projects or experiments. This freedom results in many breakthrough products, including Gmail and Google Maps. The company encourages people to share their ideas and celebrate successes through Google's various internal communication channels.

2. Shopify: Shopify is an e-commerce platform that encourages a culture of experimentation and innovation. The company has hackathons to encourage employees to experiment with new ideas and also celebrates internal entrepreneurship through an annual Build A Business contest.

3. W.L. Gore: W.L. Gore is a supplier of fabrics and materials for many industries. The company is well known for being one of the most innovative companies worldwide. They promote a culture of intrapreneurship where each employee can act as an entrepreneur in their role.

4. Netflix: Netflix encourages a culture of experimentation and encourages employees to take risks without the fear of failure. The company celebrates mistakes and encourages employees to learn from them. The company also promotes a culture of transparency and communication where decisions are shared transparently.

5. Salesforce: Salesforce is one of the leading CRM companies globally. The company promotes a culture of collaboration, encourages the sharing of ideas, and celebrates employees' achievements through programs like recognition on internal communication channels and “Ohana Awards”.

Overall, these companies have successfully implemented a culture of positive reinforcement and celebration, which has had a significant impact not only on employee satisfaction but also on the success of the organization. Celebrating success is a powerful incentive that drives organizational progress and growth, creating a long-term win-win culture for employees and the company as a whole.

```yaml
reasoning: 

The markdown document snippet does not provide useful context to respond to Mike's last message about the vastness and peacefulness of the ocean, as it discusses examples of companies that promote a culture of celebrating success.
score: low

Cross Encoder

You are a document indexer. You take markdown documents as input and you return a yaml object with "title", "keywords", "entities" and "similar_keywords" that will be used to retrieve the document at a later time.

```markdown
# A Magical Hedgehog's Night Adventure

A starlit night, where the moon is at its fullest, casting a soft glow on a quiet, quaint garden. There's a gentle rustle in the foliage, and a small, spiky creature emerges - a hedgehog.

It's not just an ordinary hedgehog, but a magical one. Its quills shimmer with a soft, iridescent light, reflecting the colors of the cosmos. The hedgehog carries a tiny, glowing lantern in its mouth, illuminating its path. 

The hedgehog embarks on a nocturnal adventure, exploring the garden. Its quills leave behind a trail of stardust, creating a mesmerizing spectacle of a miniature, magical universe right in the heart of the garden.
```

Index:

```yaml

title: A Magical Hedgehog's Night Adventure
keywords:
  - hedgehog
  - night
  - adventure
  - garden
  - starlit
  - moon
  - quills
  - stardust
  - glow
  - cosmos
  - lantern
entities:
  - hedgehog
  - moon
  - garden
similar_keywords:
  - adventure
  - exploration
  - night
  - garden
  - starlit

Document Indexer

Synthetic "textbook" data generation based on ideas from [phi-1.5](https://www.youtube.com/watch?v=24O1KcIO3FM).

You are a programming teacher tasked with explaining programming concepts by writing essays. Use the Code Snippet below as inspiration for the topic you will write about.

This "Code Snippet" section of the prompt should be randomly sampled from an appropriate corpus of code. This example is pulled from [Pytorch](https://github.com/pytorch/pytorch/blob/main/torch/multiprocessing/queue.py) - See the `code_snippet` variable on the right.

Code Snippet:

```python
{{@pb var=code_snippet}}
```

Instructions:

- Your essay should be inspired by the contents of the Code Snippet, explaining programming concepts in it.
- Always include detailed examples in your essay. Surround these with in-depth explanations and add appropriate comments in your code examples.
- If there are no interesting topics / concepts to discuss from the Code Snippet, simply respond "N/A".
- Format your response in markdown and write in the style of a textbook.

# Serialization and Proxy Classes in Python

In this essay, we will explore two important programming concepts: serialization and proxy classes, as demonstrated in the provided code snippet. We will examine how these concepts are utilized in the context of multiprocessing in Python.

## Serialization

Serialization is the process of converting an object into a format that can be easily stored, transmitted, or reconstructed later. In the code snippet, serialization is achieved using the `pickle` module, which provides a way to serialize and deserialize Python objects.

The `pickle` module allows us to convert complex Python objects, such as lists, dictionaries, and custom classes, into a byte stream. This byte stream can then be stored in a file, sent over a network, or passed between different processes. The `pickle` module provides two main functions: `dump()` and `load()`. `dump()` serializes an object and writes it to a file-like object, while `load()` reads a serialized object from a file-like object and reconstructs it.

Let's take a closer look at how serialization is used in the code snippet. The `ConnectionWrapper` class acts as a proxy for the `_multiprocessing.Connection` class. It wraps the underlying connection and provides a more convenient interface for sending and receiving objects.

```python
class ConnectionWrapper:
    def __init__(self, conn):
        self.conn = conn

    def send(self, obj):
        buf = io.BytesIO()
        ForkingPickler(buf, pickle.HIGHEST_PROTOCOL).dump(obj)
        self.send_bytes(buf.getvalue())

    def recv(self):
        buf = self.recv_bytes()
        return pickle.loads(buf)
```

In the `send()` method, the object `obj` is serialized using `pickle` and then sent over the connection. The `recv()` method receives a serialized object, deserializes it using `pickle`, and returns the reconstructed object.

Serialization is essential in multiprocessing scenarios where data needs to be passed between different processes. By serializing objects, we can ensure that the data remains intact during the transfer, regardless of the underlying communication mechanism.

## Proxy Classes

In the code snippet, the `ConnectionWrapper` class acts as a proxy for the `_multiprocessing.Connection` class. A proxy class is a class that wraps another class or object and provides additional functionality or a different interface.

The purpose of the `ConnectionWrapper` class is to intercept method calls made to the underlying connection object and add serialization functionality. It achieves this by implementing the same interface as the original connection object and forwarding method calls to it when necessary.

```python
class ConnectionWrapper:
    def __getattr__(self, name):
        if "conn" in self.__dict__:
            return getattr(self.conn, name)
        raise AttributeError(f"'{type(self).__name__}' object has no attribute 'conn'")
```

The `__getattr__()` method is a special method that is called when an attribute is accessed on an object. In this case, it intercepts attribute access and delegates it to the underlying connection object (`self.conn`) if it exists. If the attribute is not found, it raises an `AttributeError`.

By using a proxy class, we can extend or modify the behavior of an existing class without modifying its source code. This allows us to add serialization functionality to the connection object without modifying the original implementation.

## Usage in Queues

The code snippet also demonstrates the usage of serialization and proxy classes in the context of multiprocessing queues. The `Queue` and `SimpleQueue` classes inherit from the `multiprocessing.queues.Queue` and `multiprocessing.queues.SimpleQueue` classes, respectively.

In the `Queue` class, the `ConnectionWrapper` is used to wrap the reader and writer connections of the underlying queue object. This ensures that objects sent through the queue are serialized before transmission and deserialized upon receipt.

```python
class Queue(multiprocessing.queues.Queue):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self._reader: ConnectionWrapper = ConnectionWrapper(self._reader)
        self._writer: ConnectionWrapper = ConnectionWrapper(self._writer)
        self._send = self._writer.send
        self._recv = self._reader.recv
```

Similarly, in the `SimpleQueue` class, the `_make_methods()` method is overridden to replace the reader and writer connections with `ConnectionWrapper` instances.

```python
class SimpleQueue(multiprocessing.queues.SimpleQueue):
    def _make_methods(self):
        if not isinstance(self._reader, ConnectionWrapper):
            self._reader: ConnectionWrapper = ConnectionWrapper(self._reader)
            self._writer: ConnectionWrapper = ConnectionWrapper(self._writer)
        super()._make_methods()
```

By using proxy classes and serialization, the `Queue` and `SimpleQueue` classes provide an interface for sending and receiving objects between different processes in a multiprocessing environment.

## Conclusion

In this essay, we explored the concepts of serialization and proxy classes in the context of multiprocessing in Python. We learned how serialization allows us to convert complex objects into a byte stream that can be easily stored, transmitted, or reconstructed. We also discovered how proxy classes can be used to wrap existing objects and provide additional functionality or a different interface.

The code snippet provided a practical example of these concepts by demonstrating the implementation of a `ConnectionWrapper` class that adds serialization functionality to a connection object. Additionally, we saw how serialization and proxy classes are utilized in the `Queue` and `SimpleQueue` classes to facilitate inter-process communication.

Understanding serialization and proxy classes is crucial for developing efficient and scalable multiprocessing applications in Python. By leveraging these concepts, developers can ensure the integrity of data transmission and extend the functionality of existing classes without modifying their source code.

Phi-1.5 Style Textbook Generation

Prompt input variables example with `{{ @pb var }}`.

Help me brainstorm blog post ideas about {{ @pb var = topic }}.

1. "The Art of Prompt Engineering: How to Craft Engaging and Effective Prompts"
2. "The Power of Words: Exploring the Psychology Behind Effective Prompts"
3. "Mastering the Art of Prompt Engineering: Techniques and Best Practices"
4. "Unleashing Creativity: How Well-Designed Prompts Inspire Innovative Thinking"
5. "From Bland to Brilliant: Transforming Dull Prompts into Captivating Ones"

Variable Example

Example few-shot prompt to auto-generate titles from documents.

Provide short titles for markdown documents.

@pb name=example_user

The pomelo (Citrus maxima) is the largest citrus fruit from the family Rutaceae and the principal ancestor of the grapefruit. It is a natural, non-hybrid citrus fruit, native to Southeast Asia. Growing on a tree 5–15 meters (16–50 feet) in height, the fruit is 15–25 cm (6–10 in) in diameter with a usual weight of 1–2 kilograms (2–4 pounds) and has a thicker rind than a grapefruit. It is divided into 11 to 18 segments and its flesh tastes like a mild grapefruit, with little of its common bitterness. The fruit generally contains few, relatively large seeds, but some varieties have numerous seeds.

@pb name=example_assistant

Pomelo

When we think of threats to air quality, our minds often turn to images of raging wildfires engulfing landscapes and billowing smoke into the atmosphere. While wildfires undoubtedly contribute to poor air quality, there’s another player in this complex game that deserves our attention: the annual Saharan dust clouds originating from the African coast between mid-June and mid-August. These massive dust clouds, though often overlooked, have a significant impact on air quality worldwide. “Dust storms and atmospheric dust processes have attracted societal attention for millennia. In ancient Korea, for example, dust events caused concern because they were considered as God’s punishment or a warning to the ruler.” (Knippertz & Stuut, 2014) In this article, we’ll delve into the intriguing phenomenon of Saharan dust clouds and their surprising influence on the air we breathe.

Saharan Dust Clouds: A Surprising Threat to Air Quality

Document Title Generator

Javascript code interpreter example.

@pb def

{
  "name": "execute_javascript",
  "description": "Execute javascript code in a v8 browser environment",
  "parameters": {
    "type": "object",
    "properties": {
      "code": {
        "type": "string",
        "description": "The javascript code to execute"
      }
    },
    "required": ["code"]
  }
}

What is the 102nd prime number?

@pb call

{
  "name": "execute_javascript",
  "arguments": "{\n  \"code\": \"function nthPrime(n) { let primes = []; for(let i=2; primes.length<n; i++) { if (primes.every(prime => i % prime !== 0)) primes.push(i); } return primes[n-1]; } nthPrime(102);\"\n}"
}

@pb name=execute_javascript

557

The 102nd prime number is 557.

Code Interpreter

Parse the unstructured data into YAML format.

@pb name=unstructured_data

{{Short description|Air defense system project}}
{{Use dmy dates|date=July 2023}}
[[File:Teilnehmer European Sky Shield 2023-2.png|thumb|{{legend|#2d672c|Founding members (2022)}}{{legend|#30b82e|Joined in 2023}}]]

'''European Sky Shield Initiative''' ('''ESSI''') is a project to build a ground-based integrated [[Europe|European]] [[air defense]] system which includes [[anti-ballistic missile]] capability. As of July 2023, nineteen European states participate in the initiative.

== Background ==
The initiative was originally proposed by the [[Chancellor of Germany]] [[Olaf Scholz]] in August 2022.<ref name="scholz-proposal">{{cite web|title=Scholz revives vision for German-led air defense network in Europe|url=https://www.defensenews.com/global/europe/2022/08/29/scholz-revives-vision-for-german-led-air-defense-network-in-europe/|date=29 August 2022|access-date=8 July 2023|website=Defense News|archive-date=14 August 2023|archive-url=https://web.archive.org/web/20230814021953/https://www.defensenews.com/global/europe/2022/08/29/scholz-revives-vision-for-german-led-air-defense-network-in-europe/|url-status=live}}</ref> The proposal was made during the [[2022–2023 Russian strikes against Ukrainian infrastructure]] and amid increased concerns about the limited European capability to defend against such threats as the Russian [[9K720 Iskander]] [[ballistic missile]] systems deployed in [[Kaliningrad]].<ref>{{cite web|title=We have reached a defining moment in our support for Ukraine|url=https://eda.europa.eu/webzine/issue24/cover-story/we-have-reached-a-defining-moment-in-our-support-for-ukraine|access-date=8 July 2023|website=[[European Defence Agency]]|archive-date=8 July 2023|archive-url=https://web.archive.org/web/20230708182704/https://eda.europa.eu/webzine/issue24/cover-story/we-have-reached-a-defining-moment-in-our-support-for-ukraine|url-status=live}}</ref><ref name="scholz-proposal"/> In October 2022, fifteen European states ([[Belgium]], [[Bulgaria]], the [[Czech Republic]], [[Estonia]], [[Finland]], [[Germany]], [[Hungary]], [[Latvia]], [[Lithuania]], the [[Netherlands]], [[Norway]], [[Slovakia]], [[Slovenia]], [[Romania]], the [[United Kingdom]]) signed a declaration to join the German-led initiative.<ref name="essi-declaration">{{cite web|title=Germany, 14 NATO allies agree to procure air defense systems|url=https://www.dw.com/en/germany-14-nato-allies-agree-to-procure-air-defense-systems/a-63423028|date=13 October 2022|access-date=8 July 2023|website=Deutsche Welle|archive-date=5 July 2023|archive-url=https://web.archive.org/web/20230705175302/https://www.dw.com/en/germany-14-nato-allies-agree-to-procure-air-defense-systems/a-63423028|url-status=live}}</ref> It would involve joint procurement of the air defense systems.<ref name="essi-declaration"/> All founding states are also members of [[NATO]] (with Finland joining the alliance in 2023), and the initiative also aims to strengthen the [[NATO Integrated Air Defense System]].<ref>{{cite web|title=14 NATO Allies and Finland agree to boost European air defence capabilities|url=https://www.nato.int/cps/en/natohq/news_208103.htm|date=13 October 2022|access-date=8 July 2023|website=NATO|archive-date=26 July 2023|archive-url=https://web.archive.org/web/20230726113901/https://www.nato.int/cps/en/natohq/news_208103.htm|url-status=live}}</ref>

In February 2023, [[Denmark]] and [[Sweden]] joined the project.<ref>{{cite web|title=European Sky Shield Initiative gains two more participants|url=https://www.nato.int/cps/en/natohq/news_211687.htm|date=15 February 2023|access-date=8 July 2023|website=NATO|archive-date=9 July 2023|archive-url=https://web.archive.org/web/20230709110058/https://www.nato.int/cps/en/natohq/news_211687.htm|url-status=live}}</ref> In July 2023, [[Neutral and Non-Aligned European States|neutral]] states [[Austria]] and [[Switzerland]] signed the declaration to join the initiative, raising questions about the future and practicalities of their [[Neutral country|policy of neutrality]].<ref name="bbc-essi">{{cite web|title=Neutral Swiss and Austrians join Europe's Sky Shield defence|url=https://www.bbc.co.uk/news/world-europe-66130857|date=7 July 2023|access-date=8 July 2023|website=BBC News|archive-date=7 July 2023|archive-url=https://web.archive.org/web/20230707173858/https://www.bbc.co.uk/news/world-europe-66130857|url-status=live}}</ref>

[[France]], on the other hand, has been challenging the initiative, citing too much reliance in German plans on equipment and technology produced outside Europe. It was suggested that the French Government is dissatisfied that the French-Italian [[Aster (missile family)|SAMP-T]] system is excluded from ESSI.<ref>{{cite web|title=France summons allies in challenge to German-led air defence plan|url=https://www.ft.com/content/6fdcc9e6-969b-4f07-aaed-d3702790b926|date=19 June 2023|access-date=8 July 2023|publisher=Financial Times|archive-date=8 July 2023|archive-url=https://web.archive.org/web/20230708182703/https://www.ft.com/content/6fdcc9e6-969b-4f07-aaed-d3702790b926|url-status=live}}</ref> In June 2023, France made a counter-proposal and has been calling for other countries to examine the alternatives.<ref>{{cite web|title=France outlines counter-offer to Germany's anti-missile defense project|url=https://www.lemonde.fr/en/international/article/2023/06/19/france-outlines-counter-offer-to-germany-s-anti-missile-defense-project_6034034_4.html|date=19 June 2023|access-date=8 July 2023|publisher=Le Monde|archive-date=8 July 2023|archive-url=https://web.archive.org/web/20230708182703/https://www.lemonde.fr/en/international/article/2023/06/19/france-outlines-counter-offer-to-germany-s-anti-missile-defense-project_6034034_4.html|url-status=live}}</ref> As of July 2023, several major European states, namely France, [[Poland]], [[Italy]] and [[Spain]] did not make a decision to join the ESSI.

== Capability ==
[[File:Arrow anti-ballistic missile launch.jpg|thumb|Launch of an Arrow 3 missile]]
The ESSI will use multi-layered defence, with the following systems planned:<ref name="bbc-essi"/>
* Medium range: primarily [[IRIS-T|IRIS-T SLM]]
* Long range: [[MIM-104 Patriot]]
* Very long range ([[exoatmospheric]]): [[Arrow 3]]

In December 2022, the German Chancellor expressed a hope that the system will be developed in the next five years.<ref>{{citation|title=Germany's Scholz wants air defence shield in next five years - Funke|url=https://www.reuters.com/world/europe/germanys-scholz-wants-air-defence-shield-next-five-years-funke-2022-12-08/|date=8 December 2022|access-date=8 July 2023|website=Reuters|archive-date=8 July 2023|archive-url=https://web.archive.org/web/20230708185623/https://www.reuters.com/world/europe/germanys-scholz-wants-air-defence-shield-next-five-years-funke-2022-12-08/|url-status=live}}</ref> In June 2023, the German [[Bundestag]] authorized nearly €4bn for the acquisition of Arrow 3 anti-ballistic missile system from [[Israel]].<ref>{{cite web|title=Germany OKs Israeli Air Defense System Procurement|url=https://www.thedefensepost.com/2023/06/15/germany-israeli-air-defense/|date=15 June 2023|access-date=8 July 2023|website=The Defense Post|archive-date=8 July 2023|archive-url=https://web.archive.org/web/20230708193715/https://www.thedefensepost.com/2023/06/15/germany-israeli-air-defense/|url-status=live}}</ref>

== See also ==
* [[Aegis Ballistic Missile Defense System]]
* [[Strategic Defense Initiative]]
* [[National Missile Defence]]
* [[NATO missile defense system]]

== References ==
{{Reflist}}

[[Category:Anti-ballistic missiles]]
[[Category:Air defence radar networks]]
[[Category:Military in Europe]]
[[Category:Missile defense]]


```yaml
Short_description: Air defense system project
Use_dmy_dates: July 2023
Image: Teilnehmer European Sky Shield 2023-2.png
Image_description: Founding members (2022) and Joined in 2023
Title: European Sky Shield Initiative
Acronym: ESSI
Description: A project to build a ground-based integrated European air defense system which includes anti-ballistic missile capability.
Participating_states: 19
Date: July 2023
Background:
  - Initiative_proposed_by: Chancellor of Germany Olaf Scholz
    Proposal_date: August 2022
    Proposal_reason: 2022–2023 Russian strikes against Ukrainian infrastructure and Russian 9K720 Iskander ballistic missile systems deployed in Kaliningrad
    Participating_states: Belgium, Bulgaria, Czech Republic, Estonia, Finland, Germany, Hungary, Latvia, Lithuania, Netherlands, Norway, Slovakia, Slovenia, Romania, United Kingdom
    Join_date: October 2022
    Additional_participants: Denmark, Sweden
    Join_date: February 2023
    Neutral_states_participants: Austria, Switzerland
    Join_date: July 2023
    Non_participants: France, Poland, Italy, Spain
Capability:
  - Defense_type: Medium range
    Defense_system: IRIS-T SLM
  - Defense_type: Long range
    Defense_system: MIM-104 Patriot
  - Defense_type: Very long range (exoatmospheric)
    Defense_system: Arrow 3
  Development_time: 5 years
  Development_start: December 2022
  Budget: €4bn
  Acquisition_date: June 2023
  Acquisition_item: Arrow 3 anti-ballistic missile system from Israel
See_also:
  - Aegis Ballistic Missile Defense System
  - Strategic Defense Initiative
  - National Missile Defence
  - NATO missile defense system
References: 14
Categories:
  - Anti-ballistic missiles
  - Air defence radar networks
  - Military in Europe
  - Missile defense
```

Unstructured Data Parsing

Simple function calling example for a realtime weather API.

@pb def

{
  "name": "get_current_weather",
  "description": "Get the current weather",
  "parameters": {
    "type": "object",
    "properties": {
      "location": {
        "type": "string",
        "description": "The city and state, e.g. San Francisco, CA"
      },
      "format": {
        "type": "string",
        "enum": ["celsius", "fahrenheit"],
        "description": "The temperature unit to use. Infer this from the users location."
      }
    },
    "required": ["location", "format"]
  }
}

what's the whether like in SF?

@pb call

{
  "name": "get_current_weather",
  "arguments": "{\n  \"location\": \"San Francisco, CA\",\n  \"format\": \"celsius\"\n}"
}

@pb name=get_current_weather

Weather in San Francisco, CA is 22° C

The current weather in San Francisco, CA is 22°C.

Realtime Weather API

A browsing plugin example with page scrolling.

You are a helpful AI assistant. You may call available functions repeatedly before responding to the user.

@pb def

{
  "name": "browse_url",
  "description": "Browse the contents of a website and parse it into markdown. Large websites (> 400 words) will be paginated, use the pagination parameter (section) in subsequent calls to request additional sections. Website metadata and pagination info will be provided in YAML at the top of the output.",
  "parameters": {
    "type": "object",
    "properties": {
      "url": {
        "type": "string",
        "description": "The URL of the website to browse"
      },
      "section": {
        "type": "number",
        "description": "Pagination parameter to specify which section of the page to view. Defaults to 0 (the first section)"
      }
    },
    "required": ["url"]
  }
}

https://stackoverflow.com/questions/58636587/how-to-use-bert-for-long-text-classification summarize

@pb call

{
  "name": "browse_url",
  "arguments": "{\n  \"url\": \"https://stackoverflow.com/questions/58636587/how-to-use-bert-for-long-text-classification\"\n}"
}

@pb name=browse_url

---
title: How to use Bert for long text classification?
section: 0
num_sections: 4
---

We know that BERT has a max length limit of tokens = 512, So if an article has a length of much bigger than 512, such as 10000 tokens in text How can BERT be used?

You have basically three options:

1. You can cut the longer texts off and only use the first 512 Tokens. The original BERT implementation (and probably the others as well) truncates longer sequences automatically. For most cases, this option is sufficient.
2. You can split your text in multiple subtexts, classify each of them and combine the results back together ( choose the class which was predicted for most of the subtexts for example). This option is obviously more expensive.

@pb call

{
  "name": "browse_url",
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3. You can even feed the output token for each subtext (as in option 2) to another network (but you won't be able to fine-tune) as described in [this discussion](https://github.com/google-research/bert/issues/27).

I would suggest to try option 1, and only if this is not good enough to consider the other options.

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This paper compared a few different strategies: [How to Fine-Tune BERT for Text Classification?](https://arxiv.org/abs/1905.05583). On the IMDb movie review dataset, they actually found that cutting out the middle of the text (rather than truncating the beginning or the end) worked best! It even outperformed more complex "hierarchical" approaches involving breaking the article into chunks and then recombining the results.

As another anecdote, I applied BERT to the Wikipedia Personal Attacks dataset here, and found that simple truncation worked well enough that I wasn't motivated to try other approaches :)

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You can leverage from the HuggingFace Transformers library that includes the following list of Transformers that work with long texts (more than 512 tokens):

- Reformer: that combines the modeling capacity of a Transformer with an architecture that can be executed efficiently on long sequences.
- Longformer: with an attention mechanism that scales linearly with sequence length, making it easy to process documents of thousands of tokens or longer.
- Eight other recently proposed efficient Transformer models include Sparse Transformers (Child et al.,2019), Linformer (Wang et al., 2020), Sinkhorn Transformers (Tay et al., 2020b), Performers (Choromanski et al., 2020b), Synthesizers (Tay et al., 2020a), Linear Transformers (Katharopoulos et al., 2020), and BigBird (Zaheeret al., 2020).

The paper from the authors from Google Research and DeepMind tries to make a comparison between these Transformers based on Long-Range Arena "aggregated metrics":

![Performance, speed, and memory footprint of the Transformers](https://i.stack.imgur.com/TMD6h.png?s=256)

They also suggest that Longformers have better performance than Reformer when it comes to the classification task.

The StackOverflow discussion on how to use BERT for long text classification provides several strategies:

1. Truncate longer texts and only use the first 512 tokens. This is the default behavior of the original BERT implementation and is usually sufficient for most cases.

2. Split the text into multiple subtexts, classify each of them, and then combine the results. This could mean choosing the class predicted most frequently across the subtexts. However, this method is more computationally expensive.

3. Feed the output token for each subtext to another network. This method doesn't allow for fine-tuning and is described in more detail in a [GitHub discussion](https://github.com/google-research/bert/issues/27).

A paper titled [How to Fine-Tune BERT for Text Classification?](https://arxiv.org/abs/1905.05583) compared different strategies and found that removing the middle of the text worked best on the IMDb movie review dataset. This approach even outperformed more complex "hierarchical" methods that involved breaking the article into chunks and recombining the results.

The HuggingFace Transformers library includes several Transformers that work with long texts (more than 512 tokens), such as Reformer and Longformer. These models can process documents of thousands of tokens or longer. Other efficient Transformer models include Sparse Transformers, Linformer, Sinkhorn Transformers, Performers, Synthesizers, Linear Transformers, and BigBird. A comparison of these Transformers based on Long-Range Arena "aggregated metrics" suggests that Longformers perform better than Reformers in classification tasks.