Xlm Roberta Ua Distilled

🚀 SentenceTransformer based on FacebookAI/xlm-roberta-base

This is a Sentence Transformer model that maps sentences and paragraphs to a 768 - dimensional dense vector space. It can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

👉 Check out the model on GitHub.

🚀 Quick Start

This is a sentence-transformers model finetuned from FacebookAI/xlm-roberta-base. 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.

✨ Features

• Maps sentences and paragraphs to a 768 - dimensional dense vector space.
• Applicable for various NLP tasks such as semantic textual similarity, semantic search, paraphrase mining, text classification, and clustering.

📦 Installation

First install the Sentence Transformers library:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("panalexeu/xlm-roberta-ua-distilled")
# Run inference
sentences = [
    "You'd better consult the doctor.",
    'Краще проконсультуйся у лікаря.',
    'Їх позначають як Aufklärungsfahrzeug 93 та Aufklärungsfahrzeug 97 відповідно.',
]
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]

📚 Documentation

Model Details

Model Description

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': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (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})
)

Evaluation

Metrics

Knowledge Distillation

• Dataset: mse-en-ua
• Evaluated with MSEEvaluator

Semantic Similarity

• Datasets: sts17-en-en, sts17-en-ua and sts17-ua-ua
• Evaluated with EmbeddingSimilarityEvaluator

Training Details

Training Dataset

• Dataset: parallel-sentences-talks, parallel-sentences-wikimatrix, parallel-sentences-tatoeba
• Size: 523,982 training samples
• Columns: english, non_english, and label
• Approximate statistics based on the first 1000 samples:

  	english	non_english	label
  type	string	string	list
  details	min: 5 tokens mean: 21.11 tokens max: 254 tokens	min: 4 tokens mean: 23.15 tokens max: 293 tokens	size: 768 elements

• Samples:

  english	non_english	label
  Her real name is Lydia (リディア, Ridia), but she was mistaken for a boy and called Ricard.	Справжнє ім'я — Лідія, але її помилково сприйняли за хлопчика і назвали Рікард.	[0.15217968821525574, -0.17830222845077515, -0.12677159905433655, 0.22082313895225525, 0.40085524320602417, ...]
  (Applause) So he didn't just learn water.	(Аплодисменти) Він не тільки вивчив слово "вода".	[-0.1058148592710495, -0.08846072107553482, -0.2684604823589325, -0.105219267308712, 0.3050258755683899, ...]
  It is tightly integrated with SAM, the Storage and Archive Manager, and hence is often referred to as SAM-QFS.	Вона тісно інтегрована з SAM (Storage and Archive Manager), тому часто називається SAM-QFS.	[0.03270340710878372, -0.45798248052597046, -0.20090211927890778, 0.006579531356692314, -0.03178019821643829, ...]

• Loss: MSELoss

Evaluation Dataset

• Dataset: parallel-sentences-talks, parallel-sentences-wikimatrix, parallel-sentences-tatoeba
• Size: 3,838 evaluation samples
• Columns: english, non_english, and label
• Approximate statistics based on the first 1000 samples:

  	english	non_english	label
  type	string	string	list
  details	min: 5 tokens mean: 15.64 tokens max: 143 tokens	min: 5 tokens mean: 16.98 tokens max: 148 tokens	size: 768 elements

• Samples:

  english	non_english	label
  I have lost my wallet.	Я загубив гаманець.	[-0.11186987161636353, -0.03419225662946701, -0.31304317712783813, 0.0838347002863884, 0.108644500374794, ...]
  It's a pharmaceutical product.	Це фармацевтичний продукт.	[0.04133488982915878, -0.4182000756263733, -0.30786487460136414, -0.09351564198732376, -0.023946482688188553, ...]
  We've all heard of the Casual Friday thing.	Всі ми чули про «джинсову п’ятницю» (вільна форма одягу).	[-0.10697802156209946, 0.21002227067947388, -0.2513434886932373, -0.3718843460083008, 0.06871984899044037, ...]

• Loss: MSELoss

Training Hyperparameters

Non - Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• gradient_accumulation_steps: 3
• num_train_epochs: 4
• warmup_ratio: 0.1

All Hyperparameters

Training Logs

📄 License

This model is licensed under the MIT license.