hon9kon9ize / bert-large-cantonese-nli

SentenceTransformer based on hon9kon9ize/bert-large-cantonese

This is a sentence-transformers model finetuned from hon9kon9ize/bert-large-cantonese on the yue-all-nli 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
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • yue-all-nli

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("hon9kon9ize/bert-large-cantonese-nli")
# Run inference
sentences = [
    '一個印度男人經過廟宇，準備去祈禱。',
    '一個男人正前往佢嘅宗教場所。',
    '有個男人跌咗落泳池度。',
]
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-all-nli

• Dataset: yue-all-nli at f6757e9
• Size: 557,367 training samples
• Columns: anchor , positive , and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 8 tokens mean: 12.96 tokens max: 66 tokens	min: 8 tokens mean: 16.62 tokens max: 53 tokens	min: 8 tokens mean: 17.9 tokens max: 59 tokens

• Samples:

  anchor	positive	negative
  一個人騎住馬跳過一架壞咗嘅飛機。	一個人喺戶外，騎住匹馬。	有個人喺茶餐廳度，叫緊個奄列。
  啲小朋友笑住向鏡頭揮手。	度有啲小朋友。	啲細路仔個樣好愁眉苦臉。
  有個仔喺條紅色橋嘅中間玩滑板跳。	個仔做緊滑板特技。	個仔喺行人路上面滑板。

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Evaluation Dataset

yue-all-nli

• Dataset: yue-all-nli at f6757e9
• Size: 6,572 evaluation samples
• Columns: anchor , positive , and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 7 tokens mean: 24.98 tokens max: 103 tokens	min: 6 tokens mean: 13.09 tokens max: 50 tokens	min: 7 tokens mean: 13.7 tokens max: 40 tokens

• Samples:

  anchor	positive	negative
  「兩個女人攬住，仲手持外賣包裝。」	兩個女人手持包裹。	班男人喺間熟食店外面打緊交。
  「兩個著住藍色球衣嘅細路，一個背號係9，另一個係2，企喺浴室嘅木梯度，喺個鋅盤度洗手。	兩個著住有號碼球衣嘅細路仔喺度洗手。	兩個著住外套嘅小朋友返緊學。
  一個男人喺安吉利斯市舉行嘅世界博覽會活動期間，向顧客賣甜甜圈。	有個男人賣緊甜甜圈俾個客人。	一個女人喺間細細嘅咖啡店飲緊咖啡。

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy : steps
• per_device_train_batch_size : 200
• per_device_eval_batch_size : 200
• num_train_epochs : 1
• warmup_ratio : 0.1
• bf16 : True
• batch_sampler : no_duplicates

All Hyperparameters

Training Logs

Framework Versions

• Python: 3.11.2
• Sentence Transformers: 3.3.1
• Transformers: 4.46.1
• PyTorch: 2.4.0+cu121
• Accelerate: 1.0.1
• Datasets: 3.1.0
• Tokenizers: 0.20.3

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}