SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-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/all-MiniLM-L6-v2
Maximum Sequence Length: 256 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': 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("uhyeonjong/fine-tuned-petitions-model")
# Run inference
sentences = [
    '이명박 출금금지 이명박 출극금지 이명박 수사후 구속 탈탈 털어서 엄벌에 처하기 바랍니다.',
    '이명박 출금금지 이명박 출극금지 이명박 수사후 구속 탈탈 털어서 엄벌에 처하기 바랍니다.',
    '장애연금을받기위해전에것을장애6급을다시살릴주길희망합니다 청와대에서 어떻게할는지모르지만 전에는중증장애가되어는데지금효력이없다고관할부서에서 이야기하는데 이것을다시살리수가없나요 나는다시살려서장애연금을받고자하는데 해줄수없나요 ? 부탁합니다',
]
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, 1.0000, 0.5313],
#         [1.0000, 1.0000, 0.5313],
#         [0.5313, 0.5313, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 51,417 training samples
Columns: sentence_0, sentence_1, and label

Approximate statistics based on the first 1000 samples:

	sentence_0	sentence_1	label
type	string	string	int
details	min: 4 tokens mean: 175.75 tokens max: 256 tokens	min: 4 tokens mean: 175.75 tokens max: 256 tokens	0: ~1.80% 1: ~3.40% 2: ~14.50% 3: ~0.70% 4: ~1.70% 5: ~4.10% 6: ~1.20% 7: ~7.10% 8: ~1.00% 9: ~9.00% 10: ~4.50% 11: ~10.40% 12: ~9.30% 13: ~3.10% 14: ~1.40% 15: ~21.40% 16: ~5.40%

Samples:

sentence_0	sentence_1	label
`이명박출국금지 이명박의 비리관련수사를 위해 출국금지를 요청합니다.`	`이명박출국금지 이명박의 비리관련수사를 위해 출국금지를 요청합니다.`	`15`
`1월 1일은 만두를 빚는 날입니다. 가족끼리 떡국과 함께 한살 더 먹는 우리의 문화를 집에서 오순도순 만두를 빚으며 어지러운 세상 속을 헤쳐나갑시다. 솔직히 시중판매되는 만두는 맛이 없잖아요~~ 저는 계속 설사하던데.. 만두 많이 빚을 수 있게 집좀 고쳐주세용용!`	`1월 1일은 만두를 빚는 날입니다. 가족끼리 떡국과 함께 한살 더 먹는 우리의 문화를 집에서 오순도순 만두를 빚으며 어지러운 세상 속을 헤쳐나갑시다. 솔직히 시중판매되는 만두는 맛이 없잖아요~~ 저는 계속 설사하던데.. 만두 많이 빚을 수 있게 집좀 고쳐주세용용!`	`5`
`이명박 출국 금지 이명박 출국 금지합니다. 제발 부탁드립니다.`	`이명박 출국 금지 이명박 출국 금지합니다. 제발 부탁드립니다.`	`12`

Loss: SoftmaxLoss

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 32
per_device_eval_batch_size: 32
num_train_epochs: 4
fp16: True
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: 32
per_device_eval_batch_size: 32
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: 4
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
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_fused
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: round_robin
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss
0.3111	500	2.7432
0.6223	1000	2.6975
0.9334	1500	2.6793
1.2446	2000	2.6625
1.5557	2500	2.6566
1.8668	3000	2.649
2.1780	3500	2.6422
2.4891	4000	2.6375
2.8002	4500	2.6328
3.1114	5000	2.6301
3.4225	5500	2.6268
3.7337	6000	2.6232

Framework Versions

Python: 3.12.12
Sentence Transformers: 5.1.2
Transformers: 4.57.3
PyTorch: 2.9.0+cu126
Accelerate: 1.12.0
Datasets: 4.0.0
Tokenizers: 0.22.1

Citation

BibTeX

Sentence Transformers and SoftmaxLoss

@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",
}

Downloads last month: 4

Safetensors

Model size

22.7M params

Tensor type

F32

Model tree for uhyeonjong/fine-tuned-petitions-model

Base model

sentence-transformers/all-MiniLM-L6-v2

Finetuned

(751)

this model

Space using uhyeonjong/fine-tuned-petitions-model 1

Paper for uhyeonjong/fine-tuned-petitions-model

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Paper • 1908.10084 • Published Aug 27, 2019 • 12