bert-large-cantonese-sts

This is a sentence-transformers model finetuned from hon9kon9ize/bert-large-cantonese-nli on the yue-stsb, stsb and C-MTEB/STSB dataset. It maps sentences & paragraphs to a 1024-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: hon9kon9ize/bert-large-cantonese-nli
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 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': 512, 'do_lower_case': False}) with Transformer model: BertModel
  (1): Pooling({'word_embedding_dimension': 1024, '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("sentence_transformers_model_id")
# Run inference
sentences = [
    '一個細路女同一個細路仔喺度睇書。',
    '一個大啲嘅小朋友玩緊公仔，望住窗外。',
    '有個男人彈緊結他。',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

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

Evaluation

Metrics

Semantic Similarity

• Datasets: sts-dev and sts-test
• Evaluated with EmbeddingSimilarityEvaluator

Training Details

Training Dataset

yue-stsb

• Dataset: yue-stsb at 40cea5d

• Size: 5,749 training samples

• Columns: sentence1, sentence2, and score

• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 7 tokensmean: 12.24 tokensmax: 40 tokens	min: 7 tokensmean: 12.21 tokensmax: 30 tokens	min: 0.0mean: 0.45max: 1.0

• Samples:

  sentence1	sentence2	score
  架飛機正準備起飛。	一架飛機正準備起飛。	1.0
  有個男人吹緊一支好大嘅笛。	有個男人吹緊笛。	0.76
  有個男人喺批薩上面灑碎芝士。	有個男人將磨碎嘅芝士灑落一塊未焗嘅批薩上面。	0.76

• Loss: CosineSimilarityLoss with these parameters:

  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  

• Size: 16,729 training samples

• Columns: sentence1, sentence2, and score

• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 5 tokensmean: 20.29 tokensmax: 74 tokens	min: 6 tokensmean: 20.36 tokensmax: 76 tokens	min: 0.0mean: 0.52max: 1.0

• Samples:

  sentence1	sentence2	score