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hon9kon9ize/bert-large-cantonese-sts

sourceHugging Faceupdated 2y agoView on Hugging Face
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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

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:

bash
pip install -U sentence-transformers

Then you can load this model and run inference.

python
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]

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Evaluation

Metrics

Semantic Similarity
Metricsts-devsts-test
pearson_cosine0.79830.7638
spearman_cosine0.79960.7605

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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:
json
  {
      "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:
json
  {
      "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:
json
  {
      "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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
  • —fsdp_transformer_layer_cls_to_wrap: None
  • —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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

EpochStepTraining LossValidation Losssts-dev_spearman_cosinests-test_spearman_cosine
0.76341000.05490.04030.7895-
1.52672000.0270.03680.7941-
2.29013000.01870.03490.7968-
3.05344000.01190.03540.8004-
3.81685000.00760.03590.7996-
4.0524---0.7605

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
bibtex
@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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