This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2. It maps sentences & paragraphs to a 384-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: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
Maximum Sequence Length: 128 tokens
Output Dimensionality: 384 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': 128, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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("wublewobble/classifier_12")
# Run inference
sentences = [
    'Event: Shanghai Old Jazz Band 上海老爵士乐队音乐会\nDescription: Relive the golden era of Shanghai’s jazz scene with this nostalgic concert.\nVenue: Shanghai Music Hall',
    'Concert : Classical Vocals-Asian',
    'Dance : Modern/Contemporary',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Binary Classification

Dataset: test
Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.995
cosine_accuracy_threshold	0.5652
cosine_f1	0.7417
cosine_f1_threshold	0.5227
cosine_precision	0.837
cosine_recall	0.6659
cosine_ap	0.7858
cosine_mcc	0.7442

Training Details

Training Dataset

Unnamed Dataset

Size: 4,140 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 21 tokens
mean: 99.72 tokens
max: 128 tokens
min: 5 tokens
mean: 8.29 tokens
max: 21 tokens

Samples:

anchor	positive
Event: The World of Swiss Education and Summer Camps - 2024 [G][object Object]Description: Come with your children to discover Switzerland's most esteemed boarding schools, hotel management schools and summer camps on a fun-filled family adventure through the Alps, experience Swiss culture and meet admission directors in person. 3.00pm Doors open. Families are free to discover boarding schools, summer camps & network. Children’s activities begin. 3.10pm Welcome by the Ambassador of Switzerland, HE Frank Grütter. Presentations to introduce Swiss schools and summer camps. 3.30pm Lucky draw 5.00pm Close[object Object]Venue: The Embassy Room, St. Regis Hotel	`Festival/Fair : Business & Professional`
`Event: Wine Tasting and Sommelier Experience[object Object]Description: Join our expert sommelier for an immersive wine tasting experience. Sample premium wines, learn the art of wine pairing, and develop a deeper appreciation for fine wines in an intimate setting.[object Object]Venue: Wine Tasting Room`	`Lifestyle/Leisure : Service`
Event: Huayi 华艺节 2020 Storytellers' Wisdom - A Crosstalk Production 十五万大军直取西城而来[object Object]Description: What does Detective Conan and Justice Bao have in common? Can the capable Sun Wukong with his endless transformations survive in the modern society? What can Jin Yong’s stories tell you about the philosophy of ‘three’? With a focus on the Empty Fort Strategy from the classic Chinese military directives Thirty-Six Stratagems , Storytellers’ Wisdom is a lighthearted crosstalk production that enacts the various chapters of Chinese culture and history through an engaging performance filled with clever dialogue and witty humour. An original creation by the renowned Comedians Workshop from Taiwan, Storytellers’ Wisdom features a selection of the group’s best works performed by established theatre practitioners including Feng Yi-Gang and Sung Shao-Ching. Discover humorous anecdotes about life told through stories from Romance of the Three Kingdoms , Justice Bao, Sun Wukong and classics fro...	`Theatre : Comedy`

Loss: CachedMultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 92
per_device_eval_batch_size: 92
num_train_epochs: 10
warmup_ratio: 0.1
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 92
per_device_eval_batch_size: 92
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: 10
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: False
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: None
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
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	test_cosine_ap
0	0	-	0.1384
1.1111	50	2.0041	0.5899
2.2222	100	1.0715	0.6977
3.3333	150	0.668	0.7221
4.4444	200	0.4198	0.7442
5.5556	250	0.2544	0.7490
6.6667	300	0.1533	0.7736
7.7778	350	0.0994	0.7806
8.8889	400	0.066	0.7834
10.0	450	0.0491	0.7858

Framework Versions

Python: 3.11.11
Sentence Transformers: 3.4.1
Transformers: 4.48.3
PyTorch: 2.6.0+cu124
Accelerate: 1.3.0
Datasets: 3.4.1
Tokenizers: 0.21.1

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",
}

CachedMultipleNegativesRankingLoss

bibtex

@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

	anchor	positive
type	string	string
details	min: 21 tokens mean: 99.72 tokens max: 128 tokens	min: 5 tokens mean: 8.29 tokens max: 21 tokens