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: 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, '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Medical Plus application is an web based application that provides the patients with speed time to find the best clinics and doctors, such as find the nearest clinic, search for best doctors, find all specialists, get latest news about new doctors and illness, ticket prices, ask for something you want to know about your illness and find new information from specific doctor.',
    'get latest news about new doctors, such as find the nearest clinic, search for best doctors, ask for something you want to know about your illness, doctors, illness, find new information from specific doctor., ticket prices, find all specialists, Medical Plus application is an web based application that provides the patients with speed time to find the best clinics',
    'he system provides online information of blood bank and administrators can also all information about Blood bank, donor, patient request and blood requirements.The Target of the application is to make the donation process more easily, spread awareness about this process, the way to deal with it.',
]
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, 0.8653, 0.3511],
#         [0.8653, 1.0000, 0.2667],
#         [0.3511, 0.2667, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 359 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 359 samples:
sentence_0 sentence_1 label
type string string float
details
min: 16 tokens
mean: 100.48 tokens
max: 128 tokens
min: 11 tokens
mean: 95.13 tokens
max: 128 tokens
min: 0.0
mean: 0.63
max: 1.0

Samples:

sentence_0	sentence_1	label
`And we dedicated that one of the most dangerous problem, that the patient not get the right Medicine for his conditions because of commercial and capitalism polices of the Medicine industry factories and Supply and demand policy of the doctors.[object Object][object Object]SO the main problems our team will make (GHC) system to solve it, and increase the performance and integrity of healthcare in Egypt to support Egypt vision 2030.`	`demand policy of the doctors., that the patient not get the right Medicine for his conditions because of commercial, we dedicated that one of the most dangerous problem, Supply, capitalism polices of the Medicine industry factories`	`0.6`
`providing information about companies,chat online,providing different works,payment system`	`providing information about companies, providing, chat online`	`0.6`
`Using this advanced technology, we will be able to gain transparency in our elections, without compromising the privacy of voters, and we have a way to accurately demonstrate the mathematical results of elections. Also, at the request of the voter, there will be a way to allow the voter to vote online in the election and follow his vote in the ballot box to ensure that his vote is stored safely and safely without changing or changing it in any way.`	`at the request of the voter, with this advanced technology, without compromising the privacy of voters, we have a way to accurately demonstrate the mathematical results of elections. Also, there will be a way to allow the, we will be able to gain transparency in our elections`	`0.6`

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

Training Hyperparameters

Non-Default Hyperparameters

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: 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
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}
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: {}

Framework Versions

Python: 3.11.9
Sentence Transformers: 5.1.1
Transformers: 4.57.1
PyTorch: 2.9.0+cpu
Accelerate: 1.11.0
Datasets: 4.3.0
Tokenizers: 0.22.1

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",
}

	sentence_0	sentence_1	label
type	string	string	float
details	min: 16 tokens mean: 100.48 tokens max: 128 tokens	min: 11 tokens mean: 95.13 tokens max: 128 tokens	min: 0.0 mean: 0.63 max: 1.0

arafamustafa/gte-small-similarity

SentenceTransformer based on thenlper/gte-small