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tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:4140
- loss:CachedMultipleNegativesRankingLoss
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
widget:
- source_sentence: 'Event: Marriage of Plum and Jade 梅玉配
Description: Marriage of Plum and Jade Since its establishment in 1953, Fujian
Provincial Experimental Min Opera Theatre has produced numerous classic operas,
such as Marriage of Plum and Jade . For more than 53 years, Fujian Provincial
Experimental Min Opera Theatre has successfully staged performances in America,
Australia, Malaysia, Indonesia, Singapore, Taiwan, Hong Kong and Macao; receiving
accolades and recognition from the different sources. Many overseas Chinese regard
the esteemed theatre troupe as a “Cultural Messenger”. Beyond a love story,
the classic Min opera Marriage of Plum and Jade also shows us the courage against
the fetter of feudal ethics and the pursuit of freedom and love. One day, a scholar
named Xu Jinmei visited a temple in the capital city, where he met the daughter
of a governor named Su Zhenyu. They fell in love at first sight. However, Miss
Su was already betrothed to Zhou Yan, a frivolous and superficial man, whom she
hated unceasingly. The opera escalates to a suspenseful climax with the dramatic
performance of Su’s sister-in-law, Hong Fang. She brilliantly burned a building
and declared that Miss Su died accidentally in the fire. Did Miss Su perish in
the fire? Will the lovers have a happy ending? The Opera profoundly portrays
the innovative and vibrant regional characteristics of the East. Through a popular
comedic style of performance, one can enjoy the spirit and a feast of the arts.
Let’s experience the elegancy and fastidiousness of the Min Opera, and witness
the astounding not- to-be-missed show.
Venue: Esplanade Theatre'
sentences:
- 'Theatre : Opera-Asian'
- 'Dance : Salsa/Tango'
- 'Concert : Dance Party'
- source_sentence: 'Event: Street Art and Culture Festival [G]
Description: This festival brings together contemporary street art, live graffiti
performances, and culture-focused exhibitions to celebrate urban creativity, inspiring
the public to appreciate the street art movement.
Venue: Various Streets in Singapore'
sentences:
- 'Dance : Traditional/Ethnic'
- 'Festival/Fair : Community & Culture'
- 'Seminar/Workshop : Corporate'
- source_sentence: 'Event: Cultivating a Collaborative Marriage - For Better & Forever
- 9.30am (Recommended for all couples)
Description: Marriage Convention 2015 Growing Together. Staying Together. Marriage
is like a seed of love sown by two people, and its resulting growth is a representation
of the effort put into nurturing it. Just like the saying, “Marriage is a journey,
not a destination.” - it is an ongoing process that requires both parties to commit
and keep working on it, or run the risk of drifting apart. Marriage Convention
2015 brings to you “ Growing Together. Staying Together” . Hear from overseas
and local marriage experts who will share insights into how couples can build,
grow together and strengthen their marriage. Get tips and ideas from renowned
author, clinician in marriage and family therapy, Dr Sherod Miller and his wife,
Dr Phyllis, on how to achieve a thriving marriage. Marriage Convention 2015
is brought to you by Families for Life. To find out more, please visit www.toggle.sg/marriageconvention
Venue: Suntec Singapore Convention and Exhibition Centre, Summit 2, Level 3'
sentences:
- 'Concert : Pop-Western'
- 'Theatre : Children'
- 'Seminar/Workshop : Family & Social'
- source_sentence: 'Event: Night at the Movies
Description: Alex Tan Sing is undoubtedly Singapore’s most versatile, most creative
multilingual, multi-talented artiste. www.AlexTanSing.com Born & bred Singaporean,
being a Singer-Songwriter-Entertainer-Comedian-Showhost-Show Producer for well
over 20 years & now a Content Creator-Live Streamer-Edutainment Provider with
the advent of the pandemic, he returns to his very first loves --- singing & entertaining. Join
Alex for FREE as he brightens up your Saturday Night at 9pm with an hour-long
live concerts-with his music, singing, comedic impressions & witty interactions.
His entertaining Live Stream on Zoom is his way of giving back as he uplifts your
spirits & moves you with his passion during these difficult times & his genuine
love & compassion for the community. Expect vibrant uplifting originals such
as A Happy Tune, Make You Smile, The Rainbow Song, Sweet Sweet Love & Sunrise to
cover versions of songs from various genres as he creates the best online experience
& parties, together with his special guests! Catch the new release of his Music
Video against Covid-19--- Stop The Virus is a fun & easy song to educate us
all in our fight against the virus! Join him on Zoom absolutely FREE & come
dressed every Saturday night in the various themes for the best online party experience
& stand to win shopping vouchers for the Best Dressed & have fun with his interactive
games too! Come dressed to the various themes:- 29 Aug 2020 Night at the
Movies 05 Sep 2020 Disney Magic 12 Sep 2020 Saturday Night Fever 19 Sep
2020 Rock & Roll 26 Sep 2020 Back to the 80''s-Solid Gold Alex will attempt
to transport you through time & space to another era, another world with his special
renditions of perennial favourites, Top 40’s, Evergreens, Disco grooves & moves,
80’s Retro, from Broadway’s best to Disney Delights too! It will be the best
times of your lives as Alex Tan Sing creates the most memorable & entertaining
online experience!!! Fun & Free!!!
Venue: Zoom Space'
sentences:
- 'Concert : Jazz'
- 'Musical : Western'
- 'Food & Beverage : F & B Voucher'
- source_sentence: 'Event: Shanghai Old Jazz Band 上海老爵士乐队音乐会
Description: Relive the golden era of Shanghai’s jazz scene with this nostalgic
concert.
Venue: Shanghai Music Hall'
sentences:
- 'Sports : Chess'
- 'Dance : Modern/Contemporary'
- 'Concert : Classical Vocals-Asian'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- cosine_mcc
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
results:
- task:
type: binary-classification
name: Binary Classification
dataset:
name: test
type: test
metrics:
- type: cosine_accuracy
value: 0.9950316493490983
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.5651620030403137
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.7417380660954712
name: Cosine F1
- type: cosine_f1_threshold
value: 0.5227307081222534
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.8370165745856354
name: Cosine Precision
- type: cosine_recall
value: 0.6659340659340659
name: Cosine Recall
- type: cosine_ap
value: 0.7858178723117079
name: Cosine Ap
- type: cosine_mcc
value: 0.7441583162870657
name: Cosine Mcc
---
# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/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](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision 86741b4e3f5cb7765a600d3a3d55a0f6a6cb443d -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 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': 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:
```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("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]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Evaluation
### Metrics
#### Binary Classification
* Dataset: `test`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.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 |
<!--
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 4,140 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 21 tokens</li><li>mean: 99.72 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 8.29 tokens</li><li>max: 21 tokens</li></ul> |
* Samples:
| anchor | positive |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------|
| <code>Event: The World of Swiss Education and Summer Camps - 2024 [G]<br>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<br>Venue: The Embassy Room, St. Regis Hotel</code> | <code>Festival/Fair : Business & Professional</code> |
| <code>Event: Wine Tasting and Sommelier Experience<br>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.<br>Venue: Wine Tasting Room</code> | <code>Lifestyle/Leisure : Service</code> |
| <code>Event: Huayi 华艺节 2020 Storytellers' Wisdom - A Crosstalk Production 十五万大军直取西城而来<br>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...</code> | <code>Theatre : Comedy</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### 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
<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`: 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
</details>
### 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}
}
```
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