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
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity

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.

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

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 at 40cea5d
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
min: 7 tokens
mean: 12.24 tokens
max: 40 tokens
min: 7 tokens
mean: 12.21 tokens
max: 30 tokens
min: 0.0
mean: 0.45
max: 1.0

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 with these parameters:

json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}
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
min: 5 tokens
mean: 20.29 tokens
max: 74 tokens
min: 6 tokens
mean: 20.36 tokens
max: 76 tokens
min: 0.0
mean: 0.52
max: 1.0

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 with these parameters:

json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}

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
min: 8 tokens
mean: 19.76 tokens
max: 53 tokens
min: 7 tokens
mean: 19.65 tokens
max: 53 tokens
min: 0.0
mean: 0.42
max: 1.0

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 with these parameters:
json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}

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",
}

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

hon9kon9ize/bert-large-cantonese-sts

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

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Semantic Similarity

Training Details

Training Dataset

yue-stsb

Evaluation Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

hon9kon9ize/
bert-large-cantonese-sts