base_model: Tirendaz/multilingual-xlm-roberta-for-ner
datasets:
- surrey-nlp/PLOD-filtered
language:
- en
library_name: transformers
pipeline_tag: token-classification
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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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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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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