This is a sentence-transformers model finetuned from Syldehayem/all-MiniLM-L12-v2_embedder_train. 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: Syldehayem/all-MiniLM-L12-v2_embedder_train
Maximum Sequence Length: 128 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': 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})
  (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("Syldehayem/all-MiniLM-L12-v2_embedder_train")
# Run inference
sentences = [
    'Horror Short Film "Nice to Finally Meet You" | ALTER | Online Premiere',
    "The Curse of Pandora's Box Returns to #UniversalHHN 2021",
    'Mondays: The Spielberg Challenge Winner!',
]
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: 9,712 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: 3 tokens
mean: 19.7 tokens
max: 49 tokens
min: 3 tokens
mean: 19.91 tokens
max: 49 tokens
min: 4 tokens
mean: 20.27 tokens
max: 50 tokens

Samples:

sentence_0	sentence_1	sentence_2
`মেয়ে যখন মায়ের মতন`	Bidhilipi	#Shorts
`A Sci-Fi Short Film: "Voltok" - by Jonathan Vleeschower`	TheCGBros	`CGI MoCap Demo : "Finger Mocap Without Any Post Animation" by the MocapLab`
`LEAKY PIPES`	`Taking care of a baby at 15`	"Fifteen" - Short film by Sameh Alaa

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

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 50
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: 16
per_device_eval_batch_size: 16
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: 50
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}
tp_size: 0
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
0.8237	500	5.0075
1.6474	1000	4.9816
2.4712	1500	5.013
3.2949	2000	4.981
4.1186	2500	4.9981
4.9423	3000	4.9727
5.7661	3500	4.9698
6.5898	4000	4.9839
7.4135	4500	5.0001
8.2372	5000	4.9996
9.0610	5500	4.9993
9.8847	6000	4.9999
10.7084	6500	5.0015
11.5321	7000	4.9934
12.3558	7500	4.9903
13.1796	8000	4.9875
14.0033	8500	5.0018
14.8270	9000	5.0088
15.6507	9500	4.9643
16.4745	10000	4.9447
17.2982	10500	4.8911
18.1219	11000	4.8719
18.9456	11500	4.8671
19.7694	12000	4.8268
20.5931	12500	4.8195
21.4168	13000	4.7726
22.2405	13500	4.7479
23.0643	14000	4.7465
23.8880	14500	4.7776
24.7117	15000	4.7366
25.5354	15500	4.7076
26.3591	16000	4.74
27.1829	16500	4.7118
28.0066	17000	4.6797
28.8303	17500	4.7144
29.6540	18000	4.662
30.4778	18500	4.6849
31.3015	19000	4.6608
32.1252	19500	4.6844
32.9489	20000	4.6561
33.7727	20500	4.6513
34.5964	21000	4.6418
35.4201	21500	4.635
36.2438	22000	4.6418
37.0675	22500	4.62
37.8913	23000	4.615
38.7150	23500	4.6189
39.5387	24000	4.6113
40.3624	24500	4.6054
41.1862	25000	4.5824
42.0099	25500	4.5907
42.8336	26000	4.5949
43.6573	26500	4.5769
44.4811	27000	4.5758
45.3048	27500	4.5613
46.1285	28000	4.5816
46.9522	28500	4.5538
47.7759	29000	4.5645
48.5997	29500	4.5653
49.4234	30000	4.5494

Framework Versions

Python: 3.12.9
Sentence Transformers: 4.1.0
Transformers: 4.51.3
PyTorch: 2.7.0+cu126
Accelerate: 1.6.0
Datasets: 3.5.1
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",
}

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: 3 tokens mean: 19.7 tokens max: 49 tokens	min: 3 tokens mean: 19.91 tokens max: 49 tokens	min: 4 tokens mean: 20.27 tokens max: 50 tokens