semvec/pypi-packages huggingface dataset:
vic-chatbot 0.1.0
A simple chatbot library with a Flask web interface
License: MIT

Chatbot Library
===
Abstract:xxx
## Papar Information
- Title:  `paper name`


## Install & Dependence
- python
- pytorch
- numpy


## Directory Hierarchy
```
|â€”â€” chatbotlib
|    |â€”â€” chatbot.py
|    |â€”â€” setup.py
|    |â€”â€” static
|        |â€”â€” css
|            |â€”â€” style.css
|        |â€”â€” js
|            |â€”â€” script.js
|    |â€”â€” templates
|        |â€”â€” index.html
|    |â€”â€” webapp.py
|    |â€”â€” __init__.py
|â€”â€” MANIFEST.in
```
## Code Details
### Tested Platform
- software
  ```
  OS: Debian unstable (May 2021), Ubuntu LTS
  Python: 3.8.5 (anaconda)
  PyTorch: 1.7.1, 1.8.1
  ```
- hardware
  ```
  CPU: Intel Xeon 6226R
  GPU: Nvidia RTX3090 (24GB)
  ```
aclsum 0.1.2
ACLSum: A New Dataset for Aspect-based Summarization of Scientific Publications
License: MIT
Requires Python: >=3.11

# ACLSum: A New Dataset for Aspect-based Summarization of Scientific Publications

This repository contains data for our paper "ACLSum: A New Dataset for Aspect-based Summarization of Scientific Publications" and a small 
utility class to work with it.


## HuggingFace datasets

You can also use Huggin Face datasets to load ACLSum ([dataset link](https://huggingface.co/datasets/sobamchan/aclsum)).
This would be convenient if you want to train transformer models using our dataset.

Just do,

```py
from datasets import load_dataset
dataset = load_dataset("sobamchan/aclsum")
```


## Our utility class

If you want to see what's in our data more carefully, the following example code on how to use our utility class may be helpful.

You can install the library with the dataset via pip, just run,

```bash
pip install aclsum
```

then you can load the dataset from your python code as,

```py
from aclsum import ACLSum

# Load per split ("train", "val", "test")
train = ACLSum("train")

# One data sample (= paper)
document = train[0]

# Three summaries on each aspect (dict[aspect, summary])
document.summaries

# Get all the sentences from the paper (we only work with abstract, introduction, and conclusion sections) (list[str])
document.get_all_sentences() 

# You can specify sections to extract sentences from
document.get_all_sentences(["abstract", "conclusion"])

# Get highlight labels (list[0 or 1])
document.get_all_highlights()

# Get highlighted sentences (list[str])
document.get_all_highlighted_sentences()
```


## Get original PDF parses

While not all the texts are included in the final dataset (only Abstract, Introduction, and Conclusion are included), you can also get the raw output data from Grobid as following,

```py
# This will load a json file in our repo.
raw_data_from_grobid_in_dict = document.get_fulltext_parse()

# For instance you can get author information
raw_data_from_grobid_in_dict["authors"]

# Or the fulltext including other sections
# This will return a list of dicts, {"text": str, "cite_spans": list, "eq_spans": list, "section": str, "sec_num": str}
raw_data_from_grobid_in_dict["pdf_parse"]["body_text"]
```