Unlocking Named Entity Recognition in Informal Persian with ParsTwiNER

September 13, 2024

If you’re venturing into the captivating but complex world of Natural Language Processing (NLP), particularly for informal Persian, you might want to roll up your sleeves and get acquainted with ParsTwiNER. This transformer-based model is designed specifically for Named Entity Recognition (NER) on data collected from Twitter. But how do you get started? Let’s dive into the steps!

What is ParsTwiNER?

ParsTwiNER is an open, wide-ranging corpus and model crafted with care to identify named entities in informal Persian text, particularly on Twitter. With a comprehensive evaluation of its performance compared to ParsBERT, it shows impressive F1 scores across various entity types, positioning itself as a formidable tool for Persian NER.

How to Use ParsTwiNER

Ready to bring ParsTwiNER into your project? Follow these simple steps using TensorFlow 2.0:

from transformers import TFAutoModelForTokenClassification, AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained('overfittwiner-bert-base-mtl')
model = TFAutoModelForTokenClassification.from_pretrained('overfittwiner-bert-base-mtl')
twiner_mtl = pipeline('ner', model=model, tokenizer=tokenizer, ignore_labels=[])

Understanding the Code: An Analogy

Think of the code like assembling a sandwich:

Importing Libraries: This is like gathering your ingredients (bread, lettuce, cheese). You’re bringing in the necessary tools (TFAutoModel, AutoTokenizer) that will help you make the perfect sandwich.
Getting the Tokenizer: This step is akin to slicing your bread; you’re prepping your ingredients to be manageable and usable. The tokenizer helps break down the text for processing.
Loading the Model: Just as you’d choose a kitchen appliance to make your sandwich (a grill or a pan), you’re picking a model that understands the language specifics to help identify named entities.
Creating the NER Pipeline: Finally, assembling your sandwich, where all the ingredients (model, tokenizer) come together to form a delicious outcome of named entity recognition.

Results: ParsTwiNER Performance

Let’s take a peek at how ParsTwiNER stacks up against ParsBERT:

| Entity Type | ParsTwiNER F1 | ParsBERT F1 | |————-|—————-|————–| | PER | 91 | 80 | | LOC | 82 | 68 | | ORG | 69 | 55 | | EVE | 41 | 12 | | POG | 85 | – | | NAT | 82.3 | – | | Total | 81.5 | 69.5 |

Troubleshooting

Encountering issues while working with ParsTwiNER? Here are some troubleshooting tips:

Error 404: Not Found – Ensure your paths are correct when loading the model or tokenizer. They should point to the right repositories.
Incompatibility Issues – Make sure your TensorFlow version is compatible with the transformers library. Version mismatches can cause runtime errors.
Out of Memory Errors – If you run into memory issues, consider using a smaller batch size or utilizing a GPU if available.

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Final Thoughts

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.