Sbert Roberta Large Anli Mnli Snli

🚀 sbert-roberta-large-anli-mnli-snli

This is a sentence-transformers model. It maps sentences and paragraphs to a 768-dimensional dense vector space, which can be used for tasks such as clustering or semantic search.

🚀 Quick Start

This model simplifies sentence and paragraph processing by mapping them to a 768-dimensional dense vector space. It's highly useful for clustering and semantic search tasks.

✨ Features

• Vector Mapping: Converts sentences and paragraphs into 768-dimensional dense vectors.
• Versatile Applications: Ideal for clustering and semantic search.
• Robust Training: Initialized with RoBERTa-large weights and trained on ANLI, MNLI, and SNLI.

📦 Installation

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

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

model = SentenceTransformer("usc-isi/sbert-roberta-large-anli-mnli-snli")
embeddings = model.encode(sentences)
print(embeddings)

Advanced Usage

Without sentence-transformers, you can use the model like this: first, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

import torch
from transformers import AutoModel, AutoTokenizer


# Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0]  # First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)


# Sentences we want sentence embeddings for
sentences = ["This is an example sentence", "Each sentence is converted"]

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained("usc-isi/sbert-roberta-large-anli-mnli-snli")
model = AutoModel.from_pretrained("usc-isi/sbert-roberta-large-anli-mnli-snli")

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input["attention_mask"])

print("Sentence embeddings:")
print(sentence_embeddings)

📚 Documentation

Evaluation Results

See section 4.1 of our paper for evaluation results.

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: RobertaModel
  (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})
)

🔧 Technical Details

The model is weight initialized by RoBERTa-large and trained on ANLI (Nie et al., 2020), MNLI (Williams et al., 2018), and SNLI (Bowman et al., 2015) using the training_nli.py example script.

Training Details:

• Learning rate: 2e-5
• Batch size: 8
• Pooling: Mean
• Training time: ~20 hours on one NVIDIA GeForce RTX 2080 Ti

📄 License

No license information provided in the original document.

📋 Information Table

📖 Citing & Authors

For more information about the project, see our paper:

Ciosici, Manuel, et al. "Machine-Assisted Script Curation." Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations, Association for Computational Linguistics, 2021, pp. 8–17. ACLWeb, https://www.aclweb.org/anthology/2021.naacl-demos.2.

📚 References

• Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015. A large annotated corpus for learning natural language inference. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 632–642, Lisbon, Portugal. Association for Computational Linguistics.
• Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela. 2020. AdversarialNLI: A new benchmark for natural language understanding. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4885–4901, Online. Association for Computational Linguistics.
• Adina Williams, Nikita Nangia, and Samuel Bowman. 2018. A broad-coverage challenge corpus for sentence understanding through inference. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1112–1122, New Orleans, Louisiana. Association for Computational Linguistics.