---
pipeline_tag: text-classification
language: 
- multilingual
tags:
- linktransformer
- transformers
- text-classification
- tabular-classification

---

# 96abhishekarora/kn-eng-prop-m-nm

This model is part of the [LinkTransformer](https://linktransformer.github.io/) ecosystem. While rooted in the a standard HuggingFace Transformer, this specific instance is tailored for text classification tasks. It classifies input sentences or paragraphs into specific categories or labels, leveraging the power of transformer architectures.

The base model for this classifier is: bert. It is pretrained for the language: - multilingual.

Labels are mapped to integers as follows: 

{LABEL_MAP}

For best results, append ಆಸ್ತಿ ಮಾಲೀಕನ ಹೆಸರು to the name

## Usage with LinkTransformer

After installing [LinkTransformer](https://linktransformer.github.io/):

```python
pip install -U linktransformer
```

Employ the model for text classification tasks:

```python
import linktransformer as lt
df_clf_output = lt.classify_rows(df, on=["col_of_interest"], model="96abhishekarora/kn-eng-prop-m-nm")
```


## Training

### Training your own LinkTransformer Classification Model

With the provided tools, you can train a custom classification model:

```python
from linktransformer import train_clf_model

best_model_path, best_metric, label_map = train_clf_model(
    data="path_to_dataset.csv",
    model="you-model-path-or-name",
    on=["col_of_interest"],
    label_col_name="label_column_name",
    lr=5e-5,
    batch_size=16,
    epochs=3
)
```





## Evaluation Results

<!--- Describe how your model was evaluated -->

Evaluation is typically based on metrics like accuracy, F1-score, precision, and recall.

## Citing & Authors

```
@misc{arora2023linktransformer,
                  title={LinkTransformer: A Unified Package for Record Linkage with Transformer Language Models},
                  author={Abhishek Arora and Melissa Dell},
                  year={2023},
                  eprint={2309.00789},
                  archivePrefix={arXiv},
                  primaryClass={cs.CL}
}
```