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.
The preprint WikiCheck: An End-to-End Open Source Automatic Fact-Checking API Based on Wikipedia:

Uploaded model from the following repo.

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 &amp; 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 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
Maximum Sequence Length: 128 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

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]

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

arg-tech
/

bart_tuned_wikifact_check_ucu