SentenceTransformer based on tonnnnnnnnnnnnn/semantic_text_sim-v1

This is a sentence-transformers model finetuned from tonnnnnnnnnnnnn/semantic_text_sim-v1 on the sample_prep_triplet dataset. 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: tonnnnnnnnnnnnn/semantic_text_sim-v1
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- sample_prep_triplet

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': 256, 'do_lower_case': False, 'architecture': '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})
  (2): Normalize()
)

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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("tonnnnnnnnnnnnn/triplet-loss-v2")
# Run inference
sentences = [
    'Earth is under constant threat from Neighbors, invincible monsters from another dimension that destroy our way of life. At least we have the elite warriors of Border, who co-opt alien technology to fight back! Our hero Osamu Mikumo may not be the best agent, but he’ll do whatever it takes to defend life on Earth as we know it. When Osamu meets a feisty humanoid Neighbor named Yuma, everything that he thinks is right is turned on its head. Can the two natural enemies ever become friends?',
    'What is the name of a manga with a protagonist named Killing and a description about him being a monster?',
    'What is the name of the manga with the description "Earth is under constant threat from Neighbors, invincible monsters from another dimension that destroy our way of life. At least we have the elite warriors of Border, who co-opt alien technology to fight back! Our hero Osamu Mikumo may not be the best agent, but he’ll do whatever it takes to defend life on Earth as we know it. When Osamu meets a feisty humanoid Neighbor named Yuma, everything that he thinks is right is turned on its head. Can the two natural enemies ever become friends?"',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7718, 0.9976],
#         [0.7718, 1.0000, 0.8020],
#         [0.9976, 0.8020, 1.0000]])

Evaluation

Metrics

Triplet

Datasets: manga-dev and wikipedia-sections-test
Evaluated with TripletEvaluator

Metric	manga-dev	wikipedia-sections-test
cosine_accuracy	0.0078	0.0078

Training Details

Training Dataset

sample_prep_triplet

Dataset: sample_prep_triplet at 5bea00c
Size: 8,100 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 7 tokens mean: 98.92 tokens max: 241 tokens	min: 8 tokens mean: 36.73 tokens max: 234 tokens	min: 7 tokens mean: 42.38 tokens max: 234 tokens

Samples:

anchor	positive	negative
`Special chapters for Keeper of the Pearl.`	`What manga is based on Daily Lives of Highschool Boys?`	`What manga has special chapters for Keeper of the Pearl?`
“I’ll wait for you,” whispers a mysterious, red-haired woman. So goes the recurring dream Jaesin Yang has had every night since the tragic massacre at the orphanage when he was a child. When he moves to the notoriously haunted Orchid Road to delve into the meaning behind his dreams, he is nearly killed by a vengeful demon on the first night. However, he is rescued by a beautiful woman named Socheon who looks just like the woman in his dreams. Together, they join forces to help innocent demons and fight against the demonic designs of Geumgwan, the lord of all demons.	What is the name of the manga with the description "Yoshida Yuko was living a perfectly normal (if poverty-stricken) life, but at the age of fifteen, she suddenly sprouts demon horns and a tail! Her mother explains that she's actually a demon named Shadow Mistress Yuko, once sealed away and cursed so that her family would remain poor for generations. Now Yuko can break that very curse if she defeats Chiyoda Momo, a magical girl who goes to her school…but Momo is stronger than she is, knows how to manage magical powers, and is so freaking nice that she's actually helping Yuko out of scrapes. Yuko's too weak and uncoordinated to straight-up kill Momo, even if she wanted to, but she can't just be buddies with this generous magical girl, either–she has to break the curse on her family somehow. What's a demon girl to do?"	`Who is the mysterious red-haired woman in Jaesin's dream?`
Makoto Kowata is a 15 year-old witch-in-training who moves out of her parents' home in Yokohama to live with relatives in rural Aomori. She has a lot to learn about magic and living in the countryside, and she's enthusiastic to begin these new adventures. Balancing her days with both the otherworldly and the mundane, Makoto learns to cast magic spells one day and grow her own vegetables the next. With her feline familiar Chito, her nomadic sister Akane, and her cousins, Kei and Chinatsu, Makoto makes the most out of each day - whether she's making enchanted snacks, accidentally summoning a giant flock of crows or exploring ancient ruins on the back of a flying whale!	What is the name of the manga with the description "Across the world, malevolent beings known as il inhabit enchanting objects to entice curious humans toward them, and in doing so they devour the heart and freely use their victims’ bodies as puppets. To combat the threat that the il pose, hunters called Syste Behards roam the land fighting their prey to keep innocent civilians safe. One such hunter is March, who has an incredible ability capable of subduing, exorcising, and defeating ils. But with a dark power sleeping inside that threatens to overwhelm the young Syste Berhard at the first sign of falling in love, can March continue to fight the il and still remain human?"	`What is a manga about a 15 year-old witch-in-training who moves out of her parents' home in Yokohama to live with relatives in rural Aomori?`

Loss: TripletLoss with these parameters:

{
    "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
    "triplet_margin": 5
}

Evaluation Dataset

sample_prep_triplet

Dataset: sample_prep_triplet at 5bea00c
Size: 900 evaluation samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 900 samples:

	anchor	positive	negative
type	string	string	string
details	min: 7 tokens mean: 98.37 tokens max: 256 tokens	min: 7 tokens mean: 38.07 tokens max: 231 tokens	min: 7 tokens mean: 43.13 tokens max: 231 tokens

Samples:

anchor	positive	negative
`February 2000: Every personnel of Babylon Labs vanished within a single night. Overseer Otto of Schicksal dispatched a special task force to investigate the incident. No one realized that this mysterious event would quickly escalate to global proportions.`	`What is the name of the manga where the protagonists go on a relaxing trip to a waterpark?`	`What happened at Babylon Labs in February 2000?`
Before Teru’s brother died, he gave her a special cellphone connected to "Daisy", a boy who would be there to look out for her whenever she needs someone. One year later and Teru continues to send mails to the mysterious Daisy, but she still has no idea who he actually is. One day, after accidentally breaking a window at school, Teru meets the rude school janitor, Tasuku Kurosaki, who insists she work off the cost of repairing the damage. But there’s more to this grouchy caretaker than meets the eye as in reality he is the Daisy who has been watching over Teru for all this time! With no intention of letting the lonely girl know his true identity, Tasuku must attempt to help Teru without being discovered, but can he really keep up the act, and just why is he taking care of her in the first place?	`What is the story of Neo Venezia?`	`What is the name of a manga about a boy who attempts to capture his mother's last days on his phone after her death?`
`The story follows Giorno Giovana, the son Dio Brando had while possessing Jonathan Joestar's body, as he rises to the top of organized crime. Giorno wants to become a Gang Star in order to help make the world a better place. His mob group must first be won over to accept him and his ideals as they take on missions with their Stand abilities. The later half of the story boils down to a showdown between their group and the Boss as the Boss tries to erase anyone who knows about him.`	`What is a manga about a high school boy who loves to cross-dress and read BL comics?`	`What is the name of the manga that follows Giorno Giovana as he rises to the top of organized crime?`

Loss: TripletLoss with these parameters:

{
    "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
    "triplet_margin": 5
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
num_train_epochs: 4
warmup_ratio: 0.1
fp16: 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: 64
per_device_eval_batch_size: 64
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
bf16: False
fp16: True
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
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
hub_revision: None
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
include_tokens_per_second: False
include_num_input_tokens_seen: no
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: True
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	manga-dev_cosine_accuracy	wikipedia-sections-test_cosine_accuracy
0.7874	100	4.2272	3.9894	0.0111	-
1.5748	200	4.0211	3.9017	0.0122	-
2.3622	300	3.9273	3.8652	0.0111	-
3.1496	400	3.8888	3.8424	0.0078	-
3.9370	500	3.8619	3.8322	0.0078	-
-1	-1	-	-	-	0.0078

Framework Versions

Python: 3.10.0
Sentence Transformers: 5.1.2
Transformers: 4.57.1
PyTorch: 2.5.1+cu121
Accelerate: 1.11.0
Datasets: 4.4.1
Tokenizers: 0.22.1

Citation

BibTeX

Sentence Transformers

@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",
}

TripletLoss

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}