semvec/pypi-packages huggingface dataset:
allennlp-srl-bert 0.1.1
Semantic Role Labeling using AllenNLP trained BERT model
Requires Python: >=3.7

# allennlp_srl_bert

`allennlp_srl_bert` is a Python package designed for **Semantic Role Labeling (SRL)** using a fine-tuned BERT model trained with the AllenNLP library.

The main purpose of this library is to extract semantic role labels from sentences using a state-of-the-art BERT model fine-tuned for SRL tasks.

## Why Use `allennlp_srl_bert`?

Since the AllenNLP library is currently under maintenance, it often causes version conflicts with other libraries. This package provides a streamlined solution for SRL tasks, eliminating dependency issues by focusing on the fine-tuned BERT model for SRL.

## Features

- **High-Performance SRL**: Leverages a fine-tuned BERT model for extracting semantic role labels.
- **Standalone Usage**: Avoids the complexities of AllenNLP dependencies.
- **Customizable**: Easily adaptable for different NLP pipelines.

## Installation

Install `allennlp_srl_bert` using pip:

```bash
pip install allennlp_srl_bert
```
fast-ldp-mst-clustering 1.0.1
Fast_LDP_MST is an efficient density-based clustering method for large-size datasets.

# fast_ldp_mst_clustering

### *Fast LDP-MST* is an efficient density-based clustering method for large-size datasets.

>**Teng Qiu, Yongjie Li, IEEE Transactions on Knowledge and Data Engineering, 2022, DOI: 10.1109/TKDE.2022.3150403.**

Cluster analysis is widely studied and used in diverse
fields. Despite that
many clustering methods have been proposed, it is rare for a clustering
method to perform well simultaneously on the following characteristics:  
* effectiveness (in terms of accuracy);
* efficiency (in terms of speed);
* robustness (in terms of noise and parameter sensitivity);
* user-friendliness (in terms of the number of user-specified parameters,
interpretability, and reproducibility of the results, etc.). 

This work contributes to the field of clustering by providing a new solution (i.e., Fast LDP-MST) which largely improves the efficiency of LDP-MST, without sacrificing the merits of LDP-MST in effectiveness, robustness, and user-friendliness. 
Fast LDP-MST achieves a good balance among effectiveness, efficiency, robustness, and user-friendliness, and thus it could
have a certain degree of practical value in this big data era.

#### The implementation of the MATLAB version can be accessed through the link: https://github.com/Teng-Qiu-Clustering/Fast-LDP-MST-Clustering-IEEE-TKDE-2022