---
library_name: transformers
tags:
- cross-encoder
license: apache-2.0
datasets:
- disi-unibo-nlp/foodex2-clean
base_model:
- microsoft/deberta-v3-large
---


# FoodEx2 Baseterm Reranker

This is a CrossEncoder reranker model fine-tuned from [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) for the FoodEx2 domain. It is designed to re-rank candidate sentences based on their relevance and suitability for food description and classification tasks. The model has been trained on the `disi-unibo-nlp/foodex2-clean` dataset, with additional negative examples drawn from `disi-unibo-nlp/foodex2-terms`.

## Model Details

- **Model Type:** CrossEncoder Reranker
- **Base Model:** [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large)
- **Maximum Sequence Length:** 256 tokens
- **Training Epochs:** 10
- **Batch Size:** 256
- **Evaluation Steps:** 15
- **Warmup Steps:** 10

This model is optimized for reranking tasks where the goal is to select the most relevant sentence(s) from a set of candidates based on food-related descriptions. It uses a dual-input architecture to compare pairs of sentences and compute similarity scores, enabling it to accurately differentiate between subtle nuances in food terminologies.

## Training Details

The model was trained using a custom training script with the following key parameters:
- **Dataset:** `disi-unibo-nlp/foodex2-clean` for positive examples and `disi-unibo-nlp/foodex2-terms` for negatives.
- **Task Number:** 1
- **Validation Ratio:** 10%
- **Evaluation on Test Set:** Enabled
- **Loss Function:** A loss function suited for ranking tasks was applied during training.

A Docker-based training script was used, ensuring reproducibility and ease of deployment. The training process leverages GPUs to accelerate computation, with model checkpoints saved periodically.

## Evaluation

The model was evaluated on the test set with the following metrics:

| Metric              | Value      |
|:--------------------|:-----------|
| **Accuracy@1**      | 0.9603     |
| **Accuracy@3**      | 0.9958     |
| **Accuracy@5**      | 1.0000     |
| **Accuracy@10**     | 1.0000     |
| **Precision@1**     | 0.9603     |
| **Recall@1**        | 0.8472     |
| **Precision@3**     | 0.4167     |
| **Recall@3**        | 0.9859     |
| **Precision@5**     | 0.2971     |
| **Recall@5**        | 0.9974     |
| **Precision@10**    | 0.2583     |
| **Recall@10**       | 0.9996     |
| **MRR@10**          | 0.9781     |
| **NDCG@10**         | 0.9817     |
| **MAP@100**         | 0.9736     |
| **Avg Seconds per Example** | 0.00139  |

Additionally, the model achieved a **binary score** of 0.9861 on a threshold-based evaluation, with the best threshold identified at 0.5454 and an F1 score of 0.3328 at this threshold.

## Usage

To use the baseterm-reranker in your application, follow these steps:

1. **Install Dependencies:**
   ```bash
   pip install transformers torch
   ```

2. **Load the Model:**
   ```python
   from transformers import AutoTokenizer, AutoModelForSequenceClassification

   tokenizer = AutoTokenizer.from_pretrained("your-username/baseterm-reranker")
   model = AutoModelForSequenceClassification.from_pretrained("your-username/baseterm-reranker")

   # Example usage: scoring candidate sentence pairs
   inputs = tokenizer(["Your first sentence", "Your second sentence"], return_tensors="pt", padding=True, truncation=True)
   outputs = model(**inputs)
   scores = outputs.logits
   print(scores)
   ```

## Citation

If you use this model in your research, please cite the relevant works:
```bibtex
@article{deberta,
  title={DeBERTa: Decoding-enhanced BERT with Disentangled Attention},
  author={He, Pengcheng and Liu, Xiaodong and Gao, Weizhu and Chen, Jianfeng},
  journal={arXiv preprint arXiv:2006.03654},
  year={2020}
}
```

## License

This model is released under the Apache 2.0 License.