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---
tags:
- software engineering
- ner
- named-entity recognition
- token-classification
widget:
- text: >-
In the field of computer graphics, a graphics processing unit (GPU) utilizes algorithms such as ray tracing, a rendering technique, to create realistic lighting effects in applications like Adobe Acrobat and Microsoft Excel.
example_title: example 1
- text: >-
By utilizing the TensorFlow and FastAPI libraries with Python, we are optimizing neural network training on devices like the Samsung Gear S2 and Intel T5300 processor.
example_title: example 2
language:
- en
datasets:
- wikiser
license: apache-2.0
---
# Software Entity Recognition with Noise-robust Learning
We train a BERT model for the task software entity recognition (SER).
The training data leverages WikiSER, a corpus of 1.7M sentences extracted from Wikipedia.
The model uses _self-regularization_ during the finetuning process, allowing it to be robust to texts in the software domain, including misannotations, different naming conventions, and others.
The model recognizes 12 fine-grained named entities: `Algorithm`, `Application`, `Architecture`, `Data_Structure`, `Device`, `Error_Name`, `General_Concept`, `Language`,
`Library`, `License`, `Operating_System`, and `Protocol`.
| Type | Examples |
|------------------|-------------------------------------------------------|
| Algorithm | Auction algorithm, Collaborative filtering |
| Application | Adobe Acrobat, Microsoft Excel |
| Architecture | Graphics processing unit, Wishbone |
| Data_Structure | Array, Hash table, mXOR linked list |
| Device | Samsung Gear S2, iPad, Intel T5300 |
| Error Name | Buffer overflow, Memory leak |
| General_Concept | Memory management, Nouvelle AI |
| Language | C++, Java, Python, Rust |
| Library | Beautiful Soup, FastAPI |
| License | Cryptix General License, MIT License |
| Operating_System | Linux, Ubuntu, Red Hat OS, MorphOS |
| Protocol | TLS, FTPS, HTTP 404 |
## Model details
Paper: https://arxiv.org/abs/2308.10564
Code: https://github.com/taidnguyen/software_entity_recognition
Finetuned from model: `bert-base-cased`
Checkpoint for large version: https://huggingface.co/taidng/wikiser-bert-large
## How to use
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("taidng/wikiser-bert-base")
model = AutoModelForTokenClassification.from_pretrained("taidng/wikiser-bert-base")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Windows XP was originally bundled with Internet Explorer 6."
ner_results = nlp(example)
print(ner_results)
```
## Citation
```bibtex
@inproceedings{nguyen2023software,
title={Software Entity Recognition with Noise-Robust Learning},
author={Nguyen, Tai and Di, Yifeng and Lee, Joohan and Chen, Muhao and Zhang, Tianyi},
booktitle={Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering (ASE'23)},
year={2023},
organization={IEEE/ACM}
}
```
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