The FSNER (Few-Shot Named Entity Recognition) model revolutionizes the way we identify entity spans in a new domain by utilizing a train-free few-shot learning approach. This innovative method is inspired by question-answering paradigms, making it especially effective in scenarios where data is sparse. In this guide, we’ll walk you through the installation process, usage, and troubleshooting tips for implementing the FSNER model.

Overview of FSNER

Proposed in the paper Example-Based Named Entity Recognition by Morteza Ziyadi and team, FSNER is designed to perform significantly better than state-of-the-art methods, particularly when limited support examples are used. It draws upon the principles of few-shot learning, enabling effective NER even in domains with scarce data.

Installation and Example Usage

You can install and use the FSNER model in three straightforward ways:

• Install directly from PyPI: Use the following command in your terminal:

pip install fsner

• Install from source: Clone the repository and run:

python install .

• Clone the repository: Run the following and ensure to set your PYTHONPATH:

git clone https://github.com/sayef/fsner

Example Code Usage

Upon successful installation, you can utilize the FSNER model as follows:

import json
from fsner import FSNERModel, FSNERTokenizerUtils

query_texts = [
    "Does Lukes serve lunch?",
    "Chang does not speak Taiwanese very well.",
    "I like Berlin."
]

support_texts = {
    "Restaurant": [
        "What time does [E] Subway [E] open for breakfast?",
        "Is there a [E] China Garden [E] restaurant in newark?",
        ...
    ],
    "Language": [
        "Although I understood no [E] French [E], I was prepared to spend the whole day with Chien - chien.",
        ...
    ]
}

device = 'cpu'
tokenizer = FSNERTokenizerUtils('sayeffsner-bert-base-uncased')
queries = tokenizer.tokenize(query_texts).to(device)
supports = tokenizer.tokenize(list(support_texts.values())).to(device)
model = FSNERModel('sayeffsner-bert-base-uncased')
model.to(device)

p_starts, p_ends = model.predict(queries, supports)
output = tokenizer.extract_entity_from_scores(query_texts, queries, p_starts, p_ends, entity_keys=list(support_texts.keys()), thresh=0.50)

print(json.dumps(output, indent=2))

Understanding the Code: An Analogy

Think of the FSNER model as a smart librarian in a library of language. The librarian (our model) can identify books (entities) on various topics (queries) based on very few hints (support examples). Just like how you might provide the librarian some specific titles or topics (the supports) to help them find related books, you provide the FSNER model with examples of what you’re looking for in a text (the entity spans). The librarian then scans the shelves and brings back the relevant books that match your queries. In essence, it uses previous knowledge alongside minimal guidance to make accurate recommendations (predictions) about entities in new and unseen documents.

Dataset Preparation

To effectively utilize FSNER, ensure your dataset is prepared in the correct format:

1. Convert your dataset into a JSON file, ensuring each list of supports contains examples for one type of entity.
2. Wrap each entity with [E] to denote the start and end of an entity.
3. Each example should contain a single pair of [E] ... [E].

Sample dataset formats can be found in the following links:

• ontonotes5 train dataset
• ontonotes5 dev dataset

Model Training

To train your FSNER model, you can use the following command:

bash fsner trainer --pretrained-model bert-base-uncased --mode train --train-data train.json --val-data val.json --train-batch-size 6 --val-batch-size 6 --n-examples-per-entity 10 --neg-example-batch-ratio 13 --max-epochs 25 --device gpu --gpus -1 --strategy ddp

Troubleshooting

If you encounter issues while implementing the FSNER model, consider the following troubleshooting tips:

• Ensure your Python version is compatible and that you have all required dependencies installed.
• Double-check the formatting of your support examples in the dataset.
• If the model fails to predict correctly, reevaluate your training dataset and ensure it has sufficient quality examples.
• Switch between CPU and GPU to see if computational resources are causing the problem.

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Conclusion

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.