bert-large-cantonese-sts

SentenceTransformer based on hon9kon9ize/bert-large-cantonese-nli

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 <!-- at revision 140fca4e8ed46ca830b9ee0f9dec91c9c114bd5b -->
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity

<!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

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]

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* -->

Evaluation

Metrics

Semantic Similarity

• Datasets: sts-dev and sts-test
• Evaluated with <code>EmbeddingSimilarityEvaluator</code>

<!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Dataset

yue-stsb

• Dataset: yue-stsb at 40cea5d

• Size: 5,749 training samples

• Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>

• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	<ul><li>min: 7 tokens</li><li>mean: 12.24 tokens</li><li>max: 40 tokens</li></ul>	<ul><li>min: 7 tokens</li><li>mean: 12.21 tokens</li><li>max: 30 tokens</li></ul>	<ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul>

• Samples:

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

• Loss: <code>CosineSimilarityLoss</code> with these parameters:

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

• Size: 16,729 training samples

• Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>

• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	<ul><li>min: 5 tokens</li><li>mean: 20.29 tokens</li><li>max: 74 tokens</li></ul>	<ul><li>min: 6 tokens</li><li>mean: 20.36 tokens</li><li>max: 76 tokens</li></ul>	<ul><li>min: 0.0</li><li>mean: 0.52</li><li>max: 1.0</li></ul>

• Samples:

  sentence1	sentence2	score
  <code>奧巴馬登記咗參加奧巴馬醫保。 </code>	<code>美國人爭住喺限期前登記參加奧巴馬醫保計劃，</code>	<code>0.24</code>
  <code>Search ends for missing asylum-seekers</code>	<code>Search narrowed for missing man</code>	<code>0.28</code>
  <code>檢察官喺五月突然轉軚，要求公開驗屍報告，因為有利於辯方嘅康納·彼得森驗屍報告部分內容已經洩露畀媒體。</code>	<code>佢哋要求公開驗屍報告，因為彼得森腹中胎兒嘅驗屍報告中，對辯方有利嘅部分已經洩露俾傳媒。</code>	<code>0.8</code>

• Loss: <code>CosineSimilarityLoss</code> with these parameters:

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

Evaluation Dataset

Unnamed Dataset

• Size: 4,458 evaluation samples
• Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	<ul><li>min: 8 tokens</li><li>mean: 19.76 tokens</li><li>max: 53 tokens</li></ul>	<ul><li>min: 7 tokens</li><li>mean: 19.65 tokens</li><li>max: 53 tokens</li></ul>	<ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul>

• Samples:

  sentence1	sentence2	score
  <code>有個戴住安全帽嘅男人喺度跳舞。</code>	<code>有個戴住安全帽嘅男人喺度跳舞。</code>	<code>1.0</code>
  <code>一個細路仔騎緊馬。</code>	<code>個細路仔騎緊匹馬。</code>	<code>0.95</code>
  <code>有個男人餵老鼠畀條蛇食。</code>	<code>個男人餵咗隻老鼠畀條蛇食。</code>	<code>1.0</code>

• Loss: <code>CosineSimilarityLoss</code> with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 128
• per_device_eval_batch_size: 128
• num_train_epochs: 4
• warmup_ratio: 0.1
• bf16: True

All Hyperparameters

<details><summary>Click to expand</summary>

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 128
• per_device_eval_batch_size: 128
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 5e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 4
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 42
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: True
• fp16: False
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 0
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: False
• ignore_data_skip: False
• fsdp: []
• fsdp_min_num_params: 0
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• fsdp_transformer_layer_cls_to_wrap: None
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• skip_memory_metrics: True
• use_legacy_prediction_loop: False
• push_to_hub: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: False
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• include_for_metrics: []
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• dispatch_batches: None
• split_batches: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• eval_use_gather_object: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional

</details>

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",
}

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