This is a sentence-transformers model finetuned from BAAI/bge-m3. 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: BAAI/bge-m3
Maximum Sequence Length: 8192 tokens
Output Dimensionality: 1024 tokens
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': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("justmeomeo1/anime_query_embedding_model")
# Run inference
sentences = [
    "The Eto Rangers ride in Space-Time Transmitting Machine Kirinda to repair the Novel Worlds of Mugen. The Eto Rangers themselves are anthropomorphic animals, each representing one of the 12 Chinese zodiac animals (and The Twelve Branches in Buddhism). In Japan they are known as the Eto animals. The Novel Worlds are stories created by the human imagination, such as old folk tales as well as newer books. These living worlds play out repeatedly, and are necessary for the good of humanity. Princess Aura rules Mugen, which is an island continent hanging over an ocean from the skybound Novel Pole. The Great God Goal gives her power, but she may never leave the small palace island area. Their nemesis is the forgotten Spirit of Cats, Nyanma (real name, Chocolat), who was excluded from the choice of 12 protectors by being disqualified from the race (in actual legend there are many versions of this story). She seeks her revenge by distorting Novel Worlds with Jarei Monsters.When a Jarei Monster goes to a Novel World they alter the story, turning it into a different version, sometimes a parody of itself. Bakumaru, the Spirit of Mice, must use the Genmakyou mirror to reveal (Jarei Shouran!) the evil spirit once enough clues lead to its identity. Often the Eto Rangers must play out some of the story to find out who or what it is, sometimes even taking on the role of one of the characters in the tale. After defeating the creature, Kirinda is called when Bakumaru holds up his hand with one of Aura's 12 gems on it and calls out Daikourin Kirinda! Kirinda descends from a dimensional slit and purifies the evil spirit with a beam weapon, calling out Jouka! (Purification).(Source: Wikipedia)",
    'Eto Rangers, Novel Worlds, Mugen adventure',
    'Olympic Paralympic animated promotional styles',
]
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]

Training Details

Training Dataset

Unnamed Dataset

Size: 2,000 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1
type string string
details
min: 27 tokens
mean: 136.75 tokens
max: 440 tokens
min: 6 tokens
mean: 12.38 tokens
max: 43 tokens

Samples:

sentence_0	sentence_1
`Music video for the song Kingyo no John by Hotaru Light Hill's Band that was featured on NHK's Minna no Uta program.`	`Hotaru Light Hill's Band Kingyo no John NHK Minna no Uta music video`
Prologue to the second game in the Generation of Chaos RPG series. Ellile is a knight-in-training in the Kingdom of Fredbarn in the Neverland World. He and Princess Roji are secretly in love, and plan to marry. Lifile, the head of the knights, finds out, and tells Ellile that he's not strong enough to protect Roji, let alone the Kingdom. Ellile becomes frustrated, and comtemplates what to do... During the OVA, there are a few animated shorts dealing with a fourth character, Poro, her robot friend, and the GOC Next cast.(Source: ANN)	`Generation of Chaos RPG series, knight-in-training romance`
`Kouji wants to work part-time, so, when he saw a cute girl with the Pia Carrot uniform, he tried to get a job there. When he's on the way, he accidentally bumped Azusa, and with some misunderstanding, they literally hate each other. What is the surprise of Kouji when he finds that she also got a job in the Pia Carrot, facing her daily. But as time passes, he will discover that 'hate' is not the appropriate word to describe his feelings...(Source: ANN)`	`Pia Carrot uniform part-time job romantic comedy`

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

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 1
per_device_eval_batch_size: 1
num_train_epochs: 2
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 1
per_device_eval_batch_size: 1
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_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
num_train_epochs: 2
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
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
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss
0.25	500	0.0
0.5	1000	0.0
0.75	1500	0.0
1.0	2000	0.0
1.25	2500	0.0
1.5	3000	0.0
1.75	3500	0.0
2.0	4000	0.0

Framework Versions

Python: 3.10.19
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.9.1+cu126
Accelerate: 0.31.0
Datasets: 2.20.0
Tokenizers: 0.19.1

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",
}

MultipleNegativesRankingLoss