---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
---

# SentenceTransformer

Repository with the model for the implementation of WikiCheck API, end-to-end open source Automatic Fact-Checking based on Wikipedia.

The research was published in **CIKM2021** applied track:
- *Trokhymovych, Mykola, and Diego Saez-Trumper.* 
**WikiCheck: An End-to-End Open Source Automatic Fact-Checking API Based on Wikipedia.**
Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 
Association for Computing Machinery, 2021, pp. 4155–4164, CIKM ’21.
[![DOI:10.1145/3459637.3481961](https://zenodo.org/badge/DOI/10.1145/3459637.3481961.svg)](https://dl.acm.org/doi/10.1145/3459637.3481961)

- The preprint **WikiCheck: An End-to-End Open Source Automatic Fact-Checking API Based on Wikipedia**: [![DOI:10.48550/arXiv.2109.00835](https://zenodo.org/badge/DOI/10.48550/arXiv.2109.00835.svg)](
https://doi.org/10.48550/arXiv.2109.00835)


Uploaded model from the following [repo](https://github.com/trokhymovych/WikiCheck).

Site:
```
@inproceedings{10.1145/3459637.3481961,
author = {Trokhymovych, Mykola and Saez-Trumper, Diego},
title = {WikiCheck: An End-to-End Open Source Automatic Fact-Checking API Based on Wikipedia},
year = {2021},
isbn = {9781450384469},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3459637.3481961},
doi = {10.1145/3459637.3481961},
booktitle = {Proceedings of the 30th ACM International Conference on Information & Knowledge Management},
pages = {4155–4164},
numpages = {10},
keywords = {applied research, nlp, nli, wikipedia, fact-checking},
location = {Virtual Event, Queensland, Australia},
series = {CIKM '21}
}
```

This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BartModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

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

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("arg-tech/bart_tuned_wikifact_check_ucu_trokhymovych")
# Run inference
sentences = [
    'The weather is lovely today.',
    "It's so sunny outside!",
    'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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## Training Details

### Framework Versions
- Python: 3.9.6
- Sentence Transformers: 3.4.1
- Transformers: 4.44.0
- PyTorch: 2.4.0
- Accelerate: 0.33.0
- Datasets: 
- Tokenizers: 0.19.1

## Citation

### BibTeX

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