acronym-definition-detection

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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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Direct Use

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Bias, Risks, and Limitations

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Recommendations

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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)

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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Preprocessing [optional]

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Training Hyperparameters

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Results

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Summary

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Environmental Impact

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

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Technical Specifications [optional]

Model Architecture and Objective

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Citation [optional]

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