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