---
language: en
license: mit
tags:
  - sentence-transformers
  - semantic-search
  - ordinal-classification
  - word-in-context
  - multilingual
model-index:
  - name: xl-durel
    results: []
---

# Model Background

This model, **XL-DURel**, is trained on **ordinal WiC** data and it is optimized using [**AnglE Loss**](https://arxiv.org/abs/2309.12871) (Li & Li, 2023).

For more details, see our paper: [XL-DURel: Finetuning Sentence Transformers for Ordinal Word-in-Context Classification](https://arxiv.org/pdf/2507.14578)

# Reproducing Results

To reproduce the results presented in the **XL-DURel** paper, please follow the instructions in our GitHub repository:[XL-DURel Reproduction Instructions](https://github.com/sachinn12/XL-DURel)

## Usage

The recommended way to use this model, including code examples for target word encoding, please see [xl-durel.ipynb](https://github.com/sachinn12/XL-DURel/blob/main/xl-durel.ipynb) in the [GitHub repository](https://github.com/sachinn12/XL-DURel).


## Simple Method

The easiest way to use this model is with the [sentence-transformers](https://www.SBERT.net) library:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer("sachinn1/xl-durel")
embeddings = model.encode(sentences)
print(embeddings)
```

## Training
The model was trained with the parameters:

**DataLoader**:

`torch.utils.data.dataloader.DataLoader` of length 9369 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```

**Loss**:

`sentence_transformers.losses.AnglELoss.AnglELoss` with parameters:
  ```
  {'scale': 20.0, 'similarity_fct': 'pairwise_angle_sim'}
  ```

Parameters of the fit()-Method:
```
{
    "epochs": 10,
    "evaluation_steps": 2342,
    "evaluator": "WordTransformer.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
    "optimizer_params": {
        "lr": 1e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 9369,
    "weight_decay": 0.0
}
```

## Citing & Authors
```
@misc{yadav2025xldurelfinetuningsentencetransformers,
      title={XL-DURel: Finetuning Sentence Transformers for Ordinal Word-in-Context Classification}, 
      author={Sachin Yadav and Dominik Schlechtweg},
      year={2025},
      eprint={2507.14578},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2507.14578}, 
}
```