This is a sentence-transformers model trained. It maps sentences & paragraphs to a 768-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

Maximum Sequence Length: 128 tokens
Output Dimensionality: 768 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': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
  (3): 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 = [
    'τί δὲ βλέπεις τὸ κάρφος τὸ ἐν τῷ ὀφθαλμῷ τοῦ ἀδελφοῦ σου, τὴν δὲ ἐν τῷ σῷ ὀφθαλμῷ δοκὸν οὐ κατανοεῖς;',
    'Καὶ ἔλεγεν αὐτοῖς· βλέπετε τί ἀκούετε. ἐν ᾧ μέτρῳ μετρεῖτε μετρηθήσεται ὑμῖν καὶ προστεθήσεται ὑμῖν τοῖς ἀκούουσιν.',
    'ἐξῆλθεν ὁ σπείρων τοῦ σπεῖραι τὸν σπόρον αὐτοῦ· καὶ ἐν τῷ σπείρειν αὐτὸν ὃ μὲν ἔπεσεν παρὰ τὴν ὁδὸν καὶ κατεπατήθη, καὶ τὰ πετεινὰ τοῦ οὐρανοῦ κατέφαγεν αὐτό.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6792, 0.6660],
#         [0.6792, 1.0000, 0.7144],
#         [0.6660, 0.7144, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 15,772 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1
type string string
details
min: 8 tokens
mean: 52.19 tokens
max: 128 tokens
min: 8 tokens
mean: 51.18 tokens
max: 128 tokens

Samples:

sentence_0	sentence_1
`καὶ ὁ βασιλεὺς Ασα παρήγγειλεν παντὶ Ιουδα εἰς Αινακιμ, καὶ αἴρουσιν τοὺς λίθους τῆς Ραμα καὶ τὰ ξύλα αὐτῆς, ἃ ᾠκοδόμησεν Βαασα, καὶ ᾠκοδόμησεν ἐν αὐτοῖς ὁ βασιλεὺς Ασα πᾶν βουνὸν Βενιαμιν καὶ τὴν σκοπιάν. –`	`καὶ Ασα ὁ βασιλεὺς ἔλαβεν πάντα τὸν Ιουδαν καὶ ἔλαβεν τοὺς λίθους τῆς Ραμα καὶ τὰ ξύλα αὐτῆς, ἃ ᾠκοδόμησεν Βαασα, καὶ ᾠκοδόμησεν ἐν αὐτοῖς τὴν Γαβαε καὶ τὴν Μασφα. –`
`καὶ ὃς ἂν τόπος μὴ δέξηται ὑμᾶς μηδὲ ἀκούσωσιν ὑμῶν, ἐκπορευόμενοι ἐκεῖθεν ἐκτινάξατε τὸν χοῦν τὸν ὑποκάτω τῶν ποδῶν ὑμῶν εἰς μαρτύριον αὐτοῖς. Ἀμὴν λέγω ὑμῖν, ἀνεκτότερον ἔσται Σοδόμοις ἢ Γομόρροις ἐν ἡμέρᾳ κρίσεως, ἢ τῇ πόλει ἐκείνῃ.`	`Σίμωνα ὃν καὶ ὠνόμασεν Πέτρον καὶ Ἀνδρέαν τὸν ἀδελφὸν αὐτοῦ καὶ Ἰάκωβον καὶ Ἰωάννην καὶ Φίλιππον καὶ Βαρθολομαῖον`
`καὶ ἐξελθὼν εἶδεν ὁ Ἰησοῦς πολὺν ὄχλον καὶ ἐσπλαγχνίσθη ἐπ᾽ αὐτοὺς ὅτι ἦσαν ὡς πρόβατα μὴ ἔχοντα ποιμένα. καὶ ἤρξατο διδάσκειν αὐτοὺς πολλά.`	`τοῦτο δὲ ἔλεγεν πειράζων αὐτόν· αὐτὸς γὰρ ᾔδει τί ἔμελλεν ποιεῖν.`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 16
num_train_epochs: 2
fp16: True
per_device_eval_batch_size: 16
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

per_device_train_batch_size: 16
num_train_epochs: 2
max_steps: -1
learning_rate: 5e-05
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_steps: 0
optim: adamw_torch_fused
optim_args: None
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
optim_target_modules: None
gradient_accumulation_steps: 1
average_tokens_across_devices: True
max_grad_norm: 1
label_smoothing_factor: 0.0
bf16: False
fp16: True
bf16_full_eval: False
fp16_full_eval: False
tf32: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
use_liger_kernel: False
liger_kernel_config: None
use_cache: False
neftune_noise_alpha: None
torch_empty_cache_steps: None
auto_find_batch_size: False
log_on_each_node: True
logging_nan_inf_filter: True
include_num_input_tokens_seen: no
log_level: passive
log_level_replica: warning
disable_tqdm: False
project: huggingface
trackio_space_id: trackio
eval_strategy: no
per_device_eval_batch_size: 16
prediction_loss_only: True
eval_on_start: False
eval_do_concat_batches: True
eval_use_gather_object: False
eval_accumulation_steps: None
include_for_metrics: []
batch_eval_metrics: False
save_only_model: False
save_on_each_node: False
enable_jit_checkpoint: False
push_to_hub: False
hub_private_repo: None
hub_model_id: None
hub_strategy: every_save
hub_always_push: False
hub_revision: None
load_best_model_at_end: False
ignore_data_skip: False
restore_callback_states_from_checkpoint: False
full_determinism: False
seed: 42
data_seed: None
use_cpu: False
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
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_pin_memory: True
dataloader_persistent_workers: False
dataloader_prefetch_factor: None
remove_unused_columns: True
label_names: None
train_sampling_strategy: random
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
ddp_backend: None
ddp_timeout: 1800
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
deepspeed: None
debug: []
skip_memory_metrics: True
do_predict: False
resume_from_checkpoint: None
warmup_ratio: None
local_rank: -1
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss
0.5071	500	2.9299
1.0142	1000	2.0355
1.5213	1500	1.9208

Framework Versions

Python: 3.12.3
Sentence Transformers: 5.2.3
Transformers: 5.2.0
PyTorch: 2.10.0+cu128
Accelerate: 1.12.0
Datasets: 4.5.0
Tokenizers: 0.22.2

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",
}

MultipleNegativesRankingLoss