--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:16729 - loss:CosineSimilarityLoss base_model: hon9kon9ize/bert-large-cantonese-nli widget: - source_sentence: 啲狗喺雪入面玩緊。 sentences: - 呢個係我成日覺得對一年級學生好有幫助嘅例子。 - 兩隻狗喺沙灘到玩緊。 - 喺Linux系統，我用Bibble，雖然有啲缺點，但係依家得呢個係比較專業嘅選擇。 - source_sentence: 個女人整緊蛋。 sentences: - 一班老人家圍住張飯枱影相。 - 有個男人向個女人唱歌。 - 個女人係度食嘢。 - source_sentence: 一架電單車泊喺一幅畫滿城市景觀塗鴉嘅牆邊。 sentences: - 夜晚，一架電單車泊喺一幅城市壁畫隔離。 - 一隻黑白相間嘅狗喺藍色嘅水到游水。 - 個細路仔頭髮豎晒起，係咁碌落藍色滑梯。 - source_sentence: 有個男人孭住隻狗同埋一艘獨木舟。 sentences: - 隻狗孭住個男人喺獨木舟到。 - 我見我對孖仔就係咁：細路仔學說話嗰陣，都會自己發明啲獨特嘅方言。 - 「出汗就係出汗，你真係控制唔到。」 - source_sentence: 一個細路女同一個細路仔喺度睇書。 sentences: - 個女人孭住個BB。 - 有個男人彈緊結他。 - 一個大啲嘅小朋友玩緊公仔，望住窗外。 datasets: - hon9kon9ize/yue-stsb - sentence-transformers/stsb - C-MTEB/STSB pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine model-index: - name: SentenceTransformer based on hon9kon9ize/bert-large-cantonese-nli results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev type: sts-dev metrics: - type: pearson_cosine value: 0.7983233550249502 name: Pearson Cosine - type: spearman_cosine value: 0.7996394101125816 name: Spearman Cosine - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.7637579307526682 name: Pearson Cosine - type: spearman_cosine value: 0.7604840209490058 name: Spearman Cosine --- # SentenceTransformer based on hon9kon9ize/bert-large-cantonese-nli This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [hon9kon9ize/bert-large-cantonese-nli](https://huggingface.co/hon9kon9ize/bert-large-cantonese-nli) on the [yue-stsb](https://huggingface.co/datasets/hon9kon9ize/yue-stsb), [stsb](https://huggingface.co/datasets/sentence-transformers/stsb) and [C-MTEB/STSB](https://huggingface.co/datasets/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](https://huggingface.co/hon9kon9ize/bert-large-cantonese-nli) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 dimensions - **Similarity Function:** Cosine Similarity ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### 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] ``` ## Evaluation ### Metrics #### Semantic Similarity * Datasets: `sts-dev` and `sts-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | sts-dev | sts-test | |:--------------------|:-----------|:-----------| | pearson_cosine | 0.7983 | 0.7638 | | **spearman_cosine** | **0.7996** | **0.7605** | ## Training Details ### Training Dataset #### yue-stsb * Dataset: [yue-stsb](https://huggingface.co/datasets/hon9kon9ize/yue-stsb) at [40cea5d](https://huggingface.co/datasets/hon9kon9ize/yue-stsb/tree/40cea5d8e9d1aeb1498816d90d1e417bafcc96a8) * 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 | | | | * 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](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "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 | | | | * Samples: | sentence1 | sentence2 | score | |:----------------------------------------------------------------|:---------------------------------------------------------|:------------------| | 奧巴馬登記咗參加奧巴馬醫保。 | 美國人爭住喺限期前登記參加奧巴馬醫保計劃， | 0.24 | | Search ends for missing asylum-seekers | Search narrowed for missing man | 0.28 | | 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。 | 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。 | 0.8 | * Loss: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 4,458 evaluation samples * Columns: sentence1, sentence2, and score * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence1 | sentence2 | score | |:-----------------------------|:-----------------------------|:------------------| | 有個戴住安全帽嘅男人喺度跳舞。 | 有個戴住安全帽嘅男人喺度跳舞。 | 1.0 | | 一個細路仔騎緊馬。 | 個細路仔騎緊匹馬。 | 0.95 | | 有個男人餵老鼠畀條蛇食。 | 個男人餵咗隻老鼠畀條蛇食。 | 1.0 | * Loss: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) 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
Click to expand - `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
### Training Logs | Epoch | Step | Training Loss | Validation Loss | sts-dev_spearman_cosine | sts-test_spearman_cosine | |:------:|:----:|:-------------:|:---------------:|:-----------------------:|:------------------------:| | 0.7634 | 100 | 0.0549 | 0.0403 | 0.7895 | - | | 1.5267 | 200 | 0.027 | 0.0368 | 0.7941 | - | | 2.2901 | 300 | 0.0187 | 0.0349 | 0.7968 | - | | 3.0534 | 400 | 0.0119 | 0.0354 | 0.8004 | - | | 3.8168 | 500 | 0.0076 | 0.0359 | 0.7996 | - | | 4.0 | 524 | - | - | - | 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", } ```