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---
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) <!-- 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](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]
```
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## Evaluation
### Metrics
#### Semantic Similarity
* Datasets: `sts-dev` and `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](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** |
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## 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: <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>](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: <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>](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: <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>](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
<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
| 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",
}
```
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