This is a sentence-transformers model finetuned from thenlper/gte-small. 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: thenlper/gte-small
Maximum Sequence Length: 512 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': 512, '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("Alexhuou/embedder_model_STxmmluV3")
# Run inference
sentences = [
    "This question refers to the following information.\nGunpowder Weaponry: Europe vs. China\nIn Western Europe during the 1200s through the 1400s, early cannons, as heavy and as slow to fire as they were, proved useful enough in the protracted sieges that dominated warfare during this period that governments found it sufficiently worthwhile to pay for them and for the experimentation that eventually produced gunpowder weapons that were both more powerful and easier to move. By contrast, China, especially after the mid-1300s, was threatened mainly by highly mobile steppe nomads, against whom early gunpowder weapons, with their unwieldiness, proved of little utility. It therefore devoted its efforts to the improvement of horse archer units who could effectively combat the country's deadliest foe.\nAccording to this passage, why did the Chinese, despite inventing gunpowder, fail to lead in the innovation of gunpowder weaponry?",
    'This question refers to the following information.\nBy what principle of reason then, should these foreigners send in return a poisonous drug? Without meaning to say that the foreigners harbor such destructive intentions in their hearts, we yet positively assert that from their inordinate thirst after gain, they are perfectly careless about the injuries they inflict upon us! And such being the case, we should like to ask what has become of that conscience which heaven has implanted in the breasts of all men? We have heard that in your own country opium is prohibited with the utmost strictness and severity. This is a strong proof that you know full well how hurtful it is to mankind. Since you do not permit it to injure your own country, you ought not to have this injurious drug transferred to another country, and above all others, how much less to the Inner Land! Of the products which China exports to your foreign countries, there is not one which is not beneficial to mankind in some shape or other.\nLin Zexu, Chinese trade commissioner, letter to Queen Victoria, 1839\nOn which of the following arguments does the author of the passage principally base his appeal?',
    'What is the term for decisions limited by human capacity to absorb and analyse information?',
]
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]

Training Details

Training Dataset

Unnamed Dataset

Size: 5,700 training samples
Columns: sentence_0, sentence_1, and sentence_2
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 sentence_2
type string string string
details
min: 4 tokens
mean: 47.47 tokens
max: 512 tokens
min: 3 tokens
mean: 51.06 tokens
max: 512 tokens
min: 5 tokens
mean: 47.92 tokens
max: 512 tokens

Samples:

sentence_0	sentence_1	sentence_2
This question refers to the following information.[object Object]Let us not, I beseech you sir, deceive ourselves. Sir, we have done everything that could be done, to avert the storm which is now coming on. We have petitioned; we have remonstrated; we have supplicated; we have prostrated ourselves before the throne, and have implored its interposition to arrest the tyrannical hands of the ministry and Parliament. Our petitions have been slighted; our remonstrances have produced additional violence and insult; our supplications have been disregarded; and we have been spurned, with contempt, from the foot of the throne. In vain, after these things, may we indulge the fond hope of peace and reconciliation. There is no longer any room for hope.… It is in vain, sir, to extenuate the matter. Gentlemen may cry, Peace, Peace, but there is no peace. The war is actually begun! The next gale that sweeps from the north will bring to our ears the clash of resounding arms! Our brethren are already in the field! W...	This question refers to the following information.[object Object]"In one view the slaveholders have a decided advantage over all opposition. It is well to notice this advantage—the advantage of complete organization. They are organized; and yet were not at the pains of creating their organizations. The State governments, where the system of slavery exists, are complete slavery organizations. The church organizations in those States are equally at the service of slavery; while the Federal Government, with its army and navy, from the chief magistracy in Washington, to the Supreme Court, and thence to the chief marshalship at New York, is pledged to support, defend, and propagate the crying curse of human bondage. The pen, the purse, and the sword, are united against the simple truth, preached by humble men in obscure places."[object Object]Frederick Douglass, 1857[object Object]Frederick Douglass was most influenced by which of the following social movements?	`Replacing supply chains with _______ enhances the importance of product _______as well as a fundamental redesign of every activity a firm engages in that produces _______.`
`Which of the following is a true statement about program documentation?`	`The boolean expression a[i] == max`
`If both parents are affected with the same autosomal recessive disorder then the probability that each of their children will be affected equals ___.`	`Which of the following conditions shows anticipation in paternal transmission?`	`From 1988 to 1990 among heterosexuals in the US, the number of unmarried adults aged 20 to 45 who report having multiple partners has:`

Loss: TripletLoss with these parameters:
json
1{ 2 "distance_metric": "TripletDistanceMetric.EUCLIDEAN", 3 "triplet_margin": 5 4}

Training Hyperparameters

Non-Default Hyperparameters

num_train_epochs: 5
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: 8
per_device_eval_batch_size: 8
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
num_train_epochs: 5
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: 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
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: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss
0.7013	500	1.9382
1.4025	1000	1.0882
2.1038	1500	0.8478
2.8050	2000	0.5961
3.5063	2500	0.5179
4.2076	3000	0.3774
4.9088	3500	0.3646

Framework Versions

Python: 3.11.13
Sentence Transformers: 4.1.0
Transformers: 4.52.4
PyTorch: 2.6.0+cu124
Accelerate: 1.7.0
Datasets: 3.6.0
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",
}

TripletLoss

bibtex

@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}
}

	sentence_0	sentence_1	sentence_2
type	string	string	string
details	min: 4 tokens mean: 47.47 tokens max: 512 tokens	min: 3 tokens mean: 51.06 tokens max: 512 tokens	min: 5 tokens mean: 47.92 tokens max: 512 tokens