Yequan's Academic

Not All Layers of LLMs Are Necessary During Inference

Sat, 16 Aug 2025 00:00:00 +0000

Few-Shot Learner Generalizes Across AI-Generated Image Detection

Tue, 15 Jul 2025 00:00:00 +0000

52B to 1T: Lessons Learned via Tele-FLM Series

Wed, 03 Jul 2024 00:00:00 +0000

Masked Structural Growth for 2x Faster Language Model Pre-training

Tue, 07 May 2024 00:00:00 +0000

FLM Family

Mon, 01 Jan 2024 00:00:00 +0000

FLM is a large language model jointly developed by the Cognitive Team (Cofe-AI) of BAAI, together with Tsinghua University, ICT, Nanyang Technological University, and University of Electronic Science and Technology. The project aims to develop a cost-effective, fully open-source, and highly effective large model. The FLM series has evolved to its second generation at the current stage.

1. FLM-2

FLM-2 is a more significant attempt. Doing

2. FLM-101B

FLM-101B inherits the structure of FreeLM and employs the Growth Strategy (MSG) to reduce costs by more than 70%. Additionally, it utilizes loss prediction to determine the optimal hyperparameters. FLM-101B represents a significant milestone, not only validating the feasibility of individual sub-technologies but also successfully implementing them at the system level. Regarding the relationship between FLM-101B and MSG, we perceive it as analogous to the relationship between GPT-3 and the Transformer architecture—it is not merely a matter of scaling up, but rather, it signifies the first successful implementation at the system level.

For detail, please refer to the FLM-101B and How to train it with a $100,000 Budget.

3. FreeLM

FreeLM is at generation 0, with the objective of validating the feasibility of integrating relevant knowledge learning stages into language model training.

For detail, please refer to the FreeLM.

4. Concepts for Large Modes Development

Our team’s philosophy about the development of large models is:

Both system capabilities and research capabilities are essential.
Without system capabilities, it is not possible to develop large models, as it would be impossible to control costs.
Without research capabilities, one can only follow in the footsteps of others; under the circumstances where the leader in large models chooses to close the source code, it will be impossible to make further breakthroughs.

We welcome researchers with strong capabilities in both system capabilities and research capabilities to contact us!

Easter egg: This page was generated by an early version of FLM-2, without further editing.

Research without Re-search: Maximal Update Parametrization Yields Accurate Loss Prediction across Scales

Fri, 14 Apr 2023 00:00:00 +0000

The overview of Big Language Model and ChatGPT

Thu, 23 Feb 2023 09:00:00 +0000

This talk aims to clearly reveal the challenge and the powerful abilities of ChatGPT and the used GPT series language models. More importantly, we hope to discuss the future direction of both academic and industry.

Specifically, we introduce the language model, Wudao 2.0 and the academic research of language model. The slides used will be released after the stage of anonymous appraisal.

CORT: A New Baseline for Comparative Opinion Classification by Dual Prompts

Wed, 07 Dec 2022 00:00:00 +0000

EDU-Capsule: aspect-based sentiment analysis at clause level

Tue, 06 Dec 2022 00:00:00 +0000

Arabic Language Model (ALM 1.0)

Thu, 01 Dec 2022 00:00:00 +0000

In cooperation with institutes of Arabic-speaking countries, containing AASTMT, BA and IIAI, the cognitive model and data research team of Beijing Academy of Artificial Intelligence(BAAI) has published ArabicText 2022, the world’s largest Arabic text dataset among the open-source community for pre-training language models.

By collecting, aggregating and cleaning the public-available Arabic web data, we finally obtains a 200GB+ high-quality text dataset, which is the largest around the world’s open-source community. During the process of data cleaning, we applies and optimizes WudaoCleaner, an efficient and effective web text cleaning tool approved by WuDaoCorpora. At the same time, we integrate the open-source Arabic text cleaning toolkit, ArabertProcessor, into the whole cleaning pipeline as a insurance of language-specific data quality. Moreover, the informative data such as news and encyclopedia, account for more than 65% in our dataset, indicating that language models is able to gain prior knowledge easily from our corpus.

Based on ArabicText 2022, we train Arabic Language Model (ALM) which has been released on github.

Artificial Intelligence Fundamental Model Support Platform and Evaluation Technology (National Key R&D Program)

Tue, 01 Nov 2022 00:00:00 +0000

This project focuses on the development of large models, with the aim of promoting the advancement of cutting-edge technologies for ultra-large-scale intelligent models in China and facilitating the empowerment of artificial intelligence for economic and social development. It aims to build an internationally leading open-source technology system for fundamental models and establish an open innovation ecosystem centered around foundational models in artificial intelligence.

CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction

Wed, 12 Oct 2022 00:00:00 +0000

Packet Representation Learning for Traffic Classification

Sun, 14 Aug 2022 00:00:00 +0000

Interactive Information Extraction by Semantic Information Graph

Sat, 23 Jul 2022 00:00:00 +0000

A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict

Fri, 01 Jul 2022 00:00:00 +0000

Implicit Sentiment Analysis on Complicated Web Text

Sat, 01 Jan 2022 00:00:00 +0000

With the increasing complexity of the Internet context, there are more and more texts containing implicit emotions. Focusing on the complex context of the Internet, this project studies implicit sentiment analysis at multi-levels and aims to comprehensively improve the performance of implicit sentiment analysis.

Welcome to Wowchemy, the website builder for Hugo

Sun, 13 Dec 2020 00:00:00 +0000

import libr
print('hello')

Overview

The Wowchemy website builder for Hugo, along with its starter templates, is designed for professional creators, educators, and teams/organizations - although it can be used to create any kind of site
The template can be modified and customised to suit your needs. It’s a good platform for anyone looking to take control of their data and online identity whilst having the convenience to start off with a no-code solution (write in Markdown and customize with YAML parameters) and having flexibility to later add even deeper personalization with HTML and CSS
You can work with all your favourite tools and apps with hundreds of plugins and integrations to speed up your workflows, interact with your readers, and much more

Get Started

👉 Create a new site
📚 Personalize your site
💬 Chat with the Wowchemy community or Hugo community
🐦 Twitter: @wowchemy @GeorgeCushen #MadeWithWowchemy
💡 Request a feature or report a bug for Wowchemy
⬆️ Updating Wowchemy? View the Update Tutorial and Release Notes

Crowd-funded open-source software

To help us develop this template and software sustainably under the MIT license, we ask all individuals and businesses that use it to help support its ongoing maintenance and development via sponsorship.

As a token of appreciation for sponsoring, you can unlock these awesome rewards and extra features 🦄✨

Ecosystem

Hugo Academic CLI: Automatically import publications from BibTeX

Inspiration

Check out the latest demo of what you’ll get in less than 10 minutes, or view the showcase of personal, project, and business sites.

Features

Page builder - Create anything with widgets and elements
Edit any type of content - Blog posts, publications, talks, slides, projects, and more!
Create content in Markdown, Jupyter, or RStudio
Plugin System - Fully customizable color and font themes
Display Code and Math - Code highlighting and LaTeX math supported
Integrations - Google Analytics, Disqus commenting, Maps, Contact Forms, and more!
Beautiful Site - Simple and refreshing one page design
Industry-Leading SEO - Help get your website found on search engines and social media
Media Galleries - Display your images and videos with captions in a customizable gallery
Mobile Friendly - Look amazing on every screen with a mobile friendly version of your site
Multi-language - 34+ language packs including English, 中文, and Português
Multi-user - Each author gets their own profile page
Privacy Pack - Assists with GDPR
Stand Out - Bring your site to life with animation, parallax backgrounds, and scroll effects
One-Click Deployment - No servers. No databases. Only files.

Themes

Wowchemy and its templates come with automatic day (light) and night (dark) mode built-in. Alternatively, visitors can choose their preferred mode - click the moon icon in the top right of the Demo to see it in action! Day/night mode can also be disabled by the site admin in params.toml.

Choose a stunning theme and font for your site. Themes are fully customizable.

License

Released under the MIT license.

Multi-level Sentiment Analysis on Text

Sun, 23 Feb 2020 09:00:00 +0000

This talk aims to introduce the advanced studies on multiple level sentiment analysis on text. The typical levels include the document-level (i.e., RNN-Capsule) and aspect-level (i.e., ATAE-LSTM and AS-Capsules).

Data Entry Method, Device, Terminal and Computer Readable Storage Medium

Tue, 21 Jan 2020 00:00:00 +0000

The invention discloses a data entry method, a device, a terminal and a computer readable storage medium, wherein the data entry method comprises the following steps：receiving a data reporting instruction, wherein the data reporting instruction comprises a service scene identifier; inquiring a segment table identifier corresponding to the service scene identifier, and a preset storage process and a field identifier corresponding to the segment table identifier according to a preset segment table information table; acquiring original data corresponding to the service scene identification and the field identification; transmitting the original data into the preset storage process; and executing the preset storage process to generate a report data table corresponding to the segment table identification. The invention improves the maintenance efficiency of data report generation by setting the preset storage process aiming at different service scenes.

Path Travel Time Estimation using Attribute-related Hybrid Trajectories Network

Sun, 03 Nov 2019 00:00:00 +0000

Writing technical content in Markdown

Fri, 12 Jul 2019 00:00:00 +0000

Wowchemy is designed to give technical content creators a seamless experience. You can focus on the content and Wowchemy handles the rest.

Highlight your code snippets, take notes on math classes, and draw diagrams from textual representation.

On this page, you’ll find some examples of the types of technical content that can be rendered with Wowchemy.

Examples

Code

Wowchemy supports a Markdown extension for highlighting code syntax. You can customize the styles under the syntax_highlighter option in your config/_default/params.yaml file.

```python
import pandas as pd
data = pd.read_csv("data.csv")
data.head()
```

renders as

import pandas as pd
data = pd.read_csv("data.csv")
data.head()

Mindmaps

Wowchemy supports a Markdown extension for mindmaps.

Simply insert a Markdown markmap code block and optionally set the height of the mindmap as shown in the example below.

A simple mindmap defined as a Markdown list:


```markmap {height="200px"}
- Hugo Modules
  - wowchemy
  - wowchemy-plugins-netlify
  - wowchemy-plugins-netlify-cms
  - wowchemy-plugins-reveal
```

renders as

- Hugo Modules
  - wowchemy
  - wowchemy-plugins-netlify
  - wowchemy-plugins-netlify-cms
  - wowchemy-plugins-reveal

A more advanced mindmap with formatting, code blocks, and math:


```markmap
- Mindmaps
  - Links
    - [Wowchemy Docs](https://wowchemy.com/docs/)
    - [Discord Community](https://discord.gg/z8wNYzb)
    - [GitHub](https://github.com/wowchemy/wowchemy-hugo-themes)
  - Features
    - Markdown formatting
    - **inline** ~~text~~ *styles*
    - multiline
      text
    - `inline code`
    -
      ```js
      console.log('hello');
      console.log('code block');
      ```
    - Math: $x = {-b \pm \sqrt{b^2-4ac} \over 2a}$
```

renders as

- Mindmaps
  - Links
    - [Wowchemy Docs](https://wowchemy.com/docs/)
    - [Discord Community](https://discord.gg/z8wNYzb)
    - [GitHub](https://github.com/wowchemy/wowchemy-hugo-themes)
  - Features
    - Markdown formatting
    - **inline** ~~text~~ *styles*
    - multiline
      text
    - `inline code`
    -
      ```js
      console.log('hello');
      console.log('code block');
      ```
    - Math: $x = {-b \pm \sqrt{b^2-4ac} \over 2a}$

Charts

Wowchemy supports the popular Plotly format for interactive charts.

Save your Plotly JSON in your page folder, for example line-chart.json, and then add the {{< chart data="line-chart" >}} shortcode where you would like the chart to appear.

Demo:

You might also find the Plotly JSON Editor useful.

Math

Wowchemy supports a Markdown extension for $\LaTeX$ math. You can enable this feature by toggling the math option in your config/_default/params.yaml file.

To render inline or block math, wrap your LaTeX math with {{< math >}}$...${{< /math >}} or {{< math >}}$$...$${{< /math >}}, respectively. (We wrap the LaTeX math in the Wowchemy math shortcode to prevent Hugo rendering our math as Markdown. The math shortcode is new in v5.5-dev.)

Example math block:

{{< math >}}
$$
\gamma_{n} = \frac{ \left | \left (\mathbf x_{n} - \mathbf x_{n-1} \right )^T \left [\nabla F (\mathbf x_{n}) - \nabla F (\mathbf x_{n-1}) \right ] \right |}{\left \|\nabla F(\mathbf{x}_{n}) - \nabla F(\mathbf{x}_{n-1}) \right \|^2}
$$
{{< /math >}}

renders as

$$\gamma_{n} = \frac{ \left | \left (\mathbf x_{n} - \mathbf x_{n-1} \right )^T \left [\nabla F (\mathbf x_{n}) - \nabla F (\mathbf x_{n-1}) \right ] \right |}{\left \|\nabla F(\mathbf{x}_{n}) - \nabla F(\mathbf{x}_{n-1}) \right \|^2}$$

Example inline math {{< math >}}$\nabla F(\mathbf{x}_{n})${{< /math >}} renders as $\nabla F(\mathbf{x}_{n})$.

Example multi-line math using the math linebreak (\\):

{{< math >}}
$$f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\
1-p_{0}^{*} & \text{if }k=0.\end{cases}$$
{{< /math >}}

renders as

$$ f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\ 1-p_{0}^{*} & \text{if }k=0.\end{cases} $$

Diagrams

Wowchemy supports a Markdown extension for diagrams. You can enable this feature by toggling the diagram option in your config/_default/params.toml file or by adding diagram: true to your page front matter.

An example flowchart:

```mermaid
graph TD
A[Hard] -->|Text| B(Round)
B --> C{Decision}
C -->|One| D[Result 1]
C -->|Two| E[Result 2]
```

renders as

graph TD A[Hard] -->|Text| B(Round) B --> C{Decision} C -->|One| D[Result 1] C -->|Two| E[Result 2]

An example sequence diagram:

```mermaid
sequenceDiagram
Alice->>John: Hello John, how are you?
loop Healthcheck
    John->>John: Fight against hypochondria
end
Note right of John: Rational thoughts!
John-->>Alice: Great!
John->>Bob: How about you?
Bob-->>John: Jolly good!
```

renders as

sequenceDiagram Alice->>John: Hello John, how are you? loop Healthcheck John->>John: Fight against hypochondria end Note right of John: Rational thoughts! John-->>Alice: Great! John->>Bob: How about you? Bob-->>John: Jolly good!

An example Gantt diagram:

```mermaid
gantt
section Section
Completed :done,    des1, 2014-01-06,2014-01-08
Active        :active,  des2, 2014-01-07, 3d
Parallel 1   :         des3, after des1, 1d
Parallel 2   :         des4, after des1, 1d
Parallel 3   :         des5, after des3, 1d
Parallel 4   :         des6, after des4, 1d
```

renders as

gantt section Section Completed :done, des1, 2014-01-06,2014-01-08 Active :active, des2, 2014-01-07, 3d Parallel 1 : des3, after des1, 1d Parallel 2 : des4, after des1, 1d Parallel 3 : des5, after des3, 1d Parallel 4 : des6, after des4, 1d

An example class diagram:

```mermaid
classDiagram
Class01 <|-- AveryLongClass : Cool
Class03 *-- Class04
Class05 o-- Class06
Class07 .. Class08
Class09 --> C2 : Where am i?
Class09 --* C3
Class09 --|> Class07
Class07 : equals()
Class07 : Object[] elementData
Class01 : size()
Class01 : int chimp
Class01 : int gorilla
Class08 <--> C2: Cool label
```

renders as

classDiagram Class01 <|-- AveryLongClass : Cool Class03 *-- Class04 Class05 o-- Class06 Class07 .. Class08 Class09 --> C2 : Where am i? Class09 --* C3 Class09 --|> Class07 Class07 : equals() Class07 : Object[] elementData Class01 : size() Class01 : int chimp Class01 : int gorilla Class08 <--> C2: Cool label

An example state diagram:

```mermaid
stateDiagram
[*] --> Still
Still --> [*]
Still --> Moving
Moving --> Still
Moving --> Crash
Crash --> [*]
```

renders as

stateDiagram [*] --> Still Still --> [*] Still --> Moving Moving --> Still Moving --> Crash Crash --> [*]

Todo lists

You can even write your todo lists in Markdown too:

- [x] Write math example
  - [x] Write diagram example
- [ ] Do something else

renders as

Write math example
- Write diagram example
Do something else

Tables

Save your spreadsheet as a CSV file in your page’s folder and then render it by adding the Table shortcode to your page:

{{< table path="results.csv" header="true" caption="Table 1: My results" >}}

renders as

Table 1: My results
customer_id	score
1	0
2	0.5
3	1

Callouts

Academic supports a shortcode for callouts, also referred to as asides, hints, or alerts. By wrapping a paragraph in {{% callout note %}} ... {{% /callout %}}, it will render as an aside.

{{% callout note %}}
A Markdown aside is useful for displaying notices, hints, or definitions to your readers.
{{% /callout %}}

renders as

A Markdown aside is useful for displaying notices, hints, or definitions to your readers.

Spoilers

Add a spoiler to a page to reveal text, such as an answer to a question, after a button is clicked.

{{< spoiler text="Click to view the spoiler" >}}
You found me!
{{< /spoiler >}}

renders as

Click to view the spoiler

You found me!

Icons

Academic enables you to use a wide range of icons from Font Awesome and Academicons in addition to emojis.

Here are some examples using the icon shortcode to render icons:

{{< icon name="terminal" pack="fas" >}} Terminal  
{{< icon name="python" pack="fab" >}} Python  
{{< icon name="r-project" pack="fab" >}} R

renders as

Terminal
Python
R

Aspect-level Sentiment Analysis using AS-Capsules

Mon, 13 May 2019 00:00:00 +0000

Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.

Create your slides in Markdown - click the Slides button to check out the example.

Supplementary notes can be added here, including code, math, and images.

Display Jupyter Notebooks with Academic

Tue, 05 Feb 2019 00:00:00 +0000

from IPython.core.display import Image
Image('https://www.python.org/static/community_logos/python-logo-master-v3-TM-flattened.png')

print("Welcome to Academic!")

Welcome to Academic!

Install Python and JupyterLab

Install Anaconda which includes Python 3 and JupyterLab.

Alternatively, install JupyterLab with pip3 install jupyterlab.

Create or upload a Jupyter notebook

Run the following commands in your Terminal, substituting <MY-WEBSITE-FOLDER> and <SHORT-POST-TITLE> with the file path to your Academic website folder and a short title for your blog post (use hyphens instead of spaces), respectively:

mkdir -p <MY-WEBSITE-FOLDER>/content/post/<SHORT-POST-TITLE>/
cd <MY-WEBSITE-FOLDER>/content/post/<SHORT-POST-TITLE>/
jupyter lab index.ipynb

The jupyter command above will launch the JupyterLab editor, allowing us to add Academic metadata and write the content.

Edit your post metadata

The first cell of your Jupter notebook will contain your post metadata (front matter).

In Jupter, choose Markdown as the type of the first cell and wrap your Academic metadata in three dashes, indicating that it is YAML front matter:

---
title: My post's title
date: 2019-09-01

# Put any other Academic metadata here...
---

Edit the metadata of your post, using the documentation as a guide to the available options.

To set a featured image, place an image named featured into your post’s folder.

For other tips, such as using math, see the guide on writing content with Academic.

Convert notebook to Markdown

jupyter nbconvert index.ipynb --to markdown --NbConvertApp.output_files_dir=.

Example

This post was created with Jupyter. The orginal files can be found at https://github.com/gcushen/hugo-academic/tree/master/exampleSite/content/post/jupyter

Slides

Tue, 05 Feb 2019 00:00:00 +0000

Create slides in Markdown with Wowchemy

Wowchemy | Documentation

Features

Efficiently write slides in Markdown
3-in-1: Create, Present, and Publish your slides
Supports speaker notes
Mobile friendly slides

Controls

Next: Right Arrow or Space
Previous: Left Arrow
Start: Home
Finish: End
Overview: Esc
Speaker notes: S
Fullscreen: F
Zoom: Alt + Click
PDF Export

Code Highlighting

Inline code: variable

Code block:

porridge = "blueberry"
if porridge == "blueberry":
    print("Eating...")

Math

In-line math: $x + y = z$

Block math:

$$ f\left( x \right) = ;\frac{{2\left( {x + 4} \right)\left( {x - 4} \right)}}{{\left( {x + 4} \right)\left( {x + 1} \right)}} $$

Fragments

Make content appear incrementally

{{% fragment %}} One {{% /fragment %}}
{{% fragment %}} **Two** {{% /fragment %}}
{{% fragment %}} Three {{% /fragment %}}

Press Space to play!

One Two Three

A fragment can accept two optional parameters:

class: use a custom style (requires definition in custom CSS)
weight: sets the order in which a fragment appears

Speaker Notes

Add speaker notes to your presentation

{{% speaker_note %}}

- Only the speaker can read these notes
- Press `S` key to view
  {{% /speaker_note %}}

Press the S key to view the speaker notes!

Themes

black: Black background, white text, blue links (default)
white: White background, black text, blue links
league: Gray background, white text, blue links
beige: Beige background, dark text, brown links
sky: Blue background, thin dark text, blue links

night: Black background, thick white text, orange links
serif: Cappuccino background, gray text, brown links
simple: White background, black text, blue links
solarized: Cream-colored background, dark green text, blue links

Custom Slide

Customize the slide style and background

{{< slide background-image="/media/boards.jpg" >}}
{{< slide background-color="#0000FF" >}}
{{< slide class="my-style" >}}

Custom CSS Example

Let’s make headers navy colored.

Create assets/css/reveal_custom.css with:

.reveal section h1,
.reveal section h2,
.reveal section h3 {
  color: navy;
}

Questions?

Ask

Documentation

Sentiment Analysis by Capsules

Mon, 23 Apr 2018 00:00:00 +0000

Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.

Create your slides in Markdown - click the Slides button to check out the example.

Supplementary notes can be added here, including code, math, and images.

Attention-based LSTM for Aspect-level Sentiment Classification

Tue, 01 Nov 2016 00:00:00 +0000

Highlight

Cited by 2,000+ until Mar. 2023.

Mon, 01 Jan 0001 00:00:00 +0000

Yequan's Academic

Not All Layers of LLMs Are Necessary During Inference

Few-Shot Learner Generalizes Across AI-Generated Image Detection

52B to 1T: Lessons Learned via Tele-FLM Series

Masked Structural Growth for 2x Faster Language Model Pre-training

FLM Family

1. FLM-2

2. FLM-101B

3. FreeLM

4. Concepts for Large Modes Development

Research without Re-search: Maximal Update Parametrization Yields Accurate Loss Prediction across Scales

The overview of Big Language Model and ChatGPT

CORT: A New Baseline for Comparative Opinion Classification by Dual Prompts

EDU-Capsule: aspect-based sentiment analysis at clause level

Arabic Language Model (ALM 1.0)

Artificial Intelligence Fundamental Model Support Platform and Evaluation Technology (National Key R&D Program)

CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction

Packet Representation Learning for Traffic Classification

Interactive Information Extraction by Semantic Information Graph

A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict

Implicit Sentiment Analysis on Complicated Web Text

Welcome to Wowchemy, the website builder for Hugo

Overview

Get Started

Crowd-funded open-source software

❤️ Click here to become a sponsor and help support Wowchemy’s future ❤️

Ecosystem

Inspiration

Features

Themes

License

Multi-level Sentiment Analysis on Text

Data Entry Method, Device, Terminal and Computer Readable Storage Medium

Path Travel Time Estimation using Attribute-related Hybrid Trajectories Network

Writing technical content in Markdown

Examples

Code

Mindmaps

Charts

Math

Diagrams

Todo lists

Tables

Callouts

Spoilers

Icons

Did you find this page helpful? Consider sharing it 🙌

Aspect-level Sentiment Analysis using AS-Capsules

Display Jupyter Notebooks with Academic

Install Python and JupyterLab

Create or upload a Jupyter notebook

Edit your post metadata

Convert notebook to Markdown

Example

Slides

Create slides in Markdown with Wowchemy

Features

Controls

Code Highlighting

Math

Fragments

Speaker Notes

Themes

Custom Slide

Custom CSS Example

Questions?

Sentiment Analysis by Capsules

Attention-based LSTM for Aspect-level Sentiment Classification

Highlight