kaejo98/acronym-definition-detection

Model Card for Model ID

This model can detect acronyms and their corresponding definitions from a given input text.

Model Details

Model Description

The base model, Tirendaz/multilingual-xlm-roberta-for-ner, finetuned for the task of detection acronyms and definitions in input text.

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Uses

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

How to use

You can use this model with Transformers pipeline for NER.

from transformers import pipeline

from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("kaejo98/acronym-definition-detection")
model = AutoModelForTokenClassification.from_pretrained("kaejo98/acronym-definition-detection")

nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "The smart contract (SC) is a fundamental aspect of deciding which care package to go for when dealing Fit for Purpose Practice (FFPP)."

acronym_results = nlp(example)
print(acronym_results)

Abbreviation	Description
B-O	Non-acronym and definition words
B-AC	Beginning of the acronym
I-AC	Part of the acronym
B-LF	Beginning of long form (definition) of acronym
I-LF	Part of the long-form

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 12
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 1
• weight_decay=0.001
• save_steps=35000
• eval_steps = 7000
• num_train_epochs=1

Training Data

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Training Procedure

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Evaluation

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Results

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Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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