README.md

---
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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### Model Sources [optional]

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## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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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.

```python
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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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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## Glossary [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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