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}) 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("vazish/all-MiniLM-L6-v2-fine-tuned_0")
# Run inference
sentences = [
    'Tidal - High-Fidelity Music Streaming with Master Quality Audio',
    'Walmart - Everyday Low Prices on Groceries, Electronics, and More',
    'Notion - Integrated Workspace for Notes, Tasks, Databases, and Wikis',
]
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]

Evaluation

Metrics

Semantic Similarity

Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.9823
spearman_cosine	0.2608

Training Details

Training Dataset

Unnamed Dataset

Size: 49,800 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 float
details
min: 10 tokens
mean: 14.76 tokens
max: 21 tokens
min: 10 tokens
mean: 14.64 tokens
max: 21 tokens
min: 0.0
mean: 0.04
max: 1.0

Samples:

sentence_0	sentence_1	label
`TripAdvisor - Hotel Reviews, Photos, and Travel Forums`	`Docker Hub - Container Image Repository for DevOps Environments`	`0.0`
`Mastodon - Decentralized Social Media for Niche Communities`	`Allrecipes - User-Submitted Recipes, Reviews, and Cooking Tips`	`0.0`
`YouTube Music - Music Videos, Official Albums, and Live Performances`	`ESPN - Sports News, Live Scores, Stats, and Highlights`	`0.0`

Loss: CosineSimilarityLoss with these parameters:
json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 32
per_device_eval_batch_size: 32
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: 3
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
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
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	spearman_cosine
0.0372	500	0.0218	-
0.0745	1000	0.0151	-
0.1117	1500	0.0113	-
0.1490	2000	0.0076	-
0.1862	2500	0.0063	-
0.2234	3000	0.0054	-
0.2607	3500	0.0045	-
0.2979	4000	0.0041	-
0.3351	4500	0.0027	-
0.3724	5000	0.0028	-
0.4096	5500	0.0026	-
0.4469	6000	0.0021	-
0.4841	6500	0.0019	-
0.5213	7000	0.0022	-
0.5586	7500	0.0017	-
0.5958	8000	0.0018	-
0.6331	8500	0.0015	-
0.6703	9000	0.0015	-
0.7075	9500	0.0018	-
0.7448	10000	0.0014	-
0.7820	10500	0.0017	-
0.8192	11000	0.0012	-
0.8565	11500	0.0014	-
0.8937	12000	0.001	-
0.9310	12500	0.0011	-
0.9682	13000	0.001	-
1.0054	13500	0.0009	-
1.0427	14000	0.0011	-
1.0799	14500	0.001	-
1.1172	15000	0.0009	-
1.1544	15500	0.0008	-
1.1916	16000	0.001	-
1.2289	16500	0.0011	-
1.2661	17000	0.0011	-
1.3033	17500	0.0006	-
1.3406	18000	0.0011	-
1.3778	18500	0.0008	-
1.4151	19000	0.0011	-
1.4523	19500	0.0009	-
1.4895	20000	0.0011	-
1.5268	20500	0.0009	-
1.5640	21000	0.0009	-
1.6013	21500	0.0008	-
1.6385	22000	0.0005	-
1.6757	22500	0.001	-
1.7130	23000	0.0008	-
1.7502	23500	0.0007	-
1.7874	24000	0.0007	-
1.8247	24500	0.0008	-
1.8619	25000	0.001	-
1.8992	25500	0.0009	-
1.9364	26000	0.0008	-
1.9736	26500	0.0009	-
2.0109	27000	0.0007	-
2.0481	27500	0.0006	-
2.0854	28000	0.0007	-
2.1226	28500	0.0006	-
2.1598	29000	0.0007	-
2.1971	29500	0.001	-
2.2343	30000	0.0006	-
2.2715	30500	0.0006	-
2.3088	31000	0.001	-
2.3460	31500	0.0007	-
2.3833	32000	0.0008	-
2.4205	32500	0.0006	-
2.4577	33000	0.0007	-
2.4950	33500	0.0007	-
2.5322	34000	0.001	-
2.5694	34500	0.0007	-
2.6067	35000	0.0007	-
2.6439	35500	0.0008	-
2.6812	36000	0.0007	-
2.7184	36500	0.0006	-
2.7556	37000	0.0007	-
2.7929	37500	0.0007	-
2.8301	38000	0.0005	-
2.8674	38500	0.0009	-
2.9046	39000	0.0006	-
2.9418	39500	0.0007	-
2.9791	40000	0.0008	-
-1	-1	-	0.2608

Framework Versions

Python: 3.11.11
Sentence Transformers: 3.4.1
Transformers: 4.48.2
PyTorch: 2.5.1+cu124
Accelerate: 1.3.0
Datasets: 3.2.0
Tokenizers: 0.21.0

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",
}

	sentence_0	sentence_1	label
type	string	string	float
details	min: 10 tokens mean: 14.76 tokens max: 21 tokens	min: 10 tokens mean: 14.64 tokens max: 21 tokens	min: 0.0 mean: 0.04 max: 1.0