This is a sentence-transformers model finetuned from bowphs/SPhilBerta. 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
Base model: bowphs/SPhilBerta
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}) with Transformer model: RobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, '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})
)

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("julian-schelb/SPhilBerta-latin-intertextuality")
# Run inference
sentences = [
    'Query: cuius rei argumenta sunt nummi ueteres, in quibus est cum duplici fronte Ianus et in altera parte nauis, sicut idem poeta subiecit: at bona posteritas puppem formauit in aere hospitis aduentum testificata dei.',
    'Candidate: at bona posteritas puppem formauit in aere,  hospitis aduentum testificata dei.',
    'Candidate: cum enim paupertatis una eademque sit vis, quidnam dici potest, quam ob rem  Fabricio tolerabilis ea fuerit, alii negent se ferre posse?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

Unnamed Dataset

Size: 1,122 training samples
Columns: query, match, and label
Approximate statistics based on the first 1000 samples:
query match label
type string string int
details
min: 10 tokens
mean: 41.73 tokens
max: 125 tokens
min: 6 tokens
mean: 31.92 tokens
max: 128 tokens
0: ~90.80%
1: ~9.20%

Samples:

query	match	label
`Query: nam si, ut ipsa petit maiestas cognita rerum, - dicendum est, deus ille fuit, deus, inclute Memmi`	`Candidate: ipse deum genitor caelo terrisque canebat.`	`0`
`Query: uerum id genus sacrificii ab Hercule, cum ex Hispania rediret, dicitur esse sublatum, ritu tamen permanente ut pro ueris hominibus imagines iacerentur e scirpo, ut Ouidius in Fastis docet: donec in haec uenit Tirynthius arua, quotannis tristia Leucadio sacra peracta modo.`	`Candidate: Vesper adest, iuuenes, consurgite: Vesper Olympo expectata diu uix tandem lumina tollit.`	`0`
`Query: Quod in principio templi Ezechielis debui dicere, nunc praepostero ordine in fine dicturus sum, illius uersiculi memor: Hic labor ille domus, et inextricabilis error.`	`Candidate: Tu, ut antea fecisti, velim, si qui erunt ad quos aliquid scribendum a me existimes, ipse conficias.`	`0`

Loss: OnlineContrastiveLoss

Evaluation Dataset

Unnamed Dataset

Size: 330 evaluation samples
Columns: query, match, and label
Approximate statistics based on the first 330 samples:
query match label
type string string int
details
min: 18 tokens
mean: 38.7 tokens
max: 74 tokens
min: 7 tokens
mean: 31.88 tokens
max: 105 tokens
0: ~90.91%
1: ~9.09%

Samples:

query	match	label
`Query: quanti enim operantur terram et exercent uomere et tamen multis inpedientibus causis egestate conficiuntur et penuria!`	`Candidate: hoc cum ceterae gentes sic arbitrantur, tum ipsis Siculis ita persuasum est ut in animis eorum insitum atque innatum esse videatur.`	`0`
`Query: nam quia tum Corybautes galearum tinnitibus et scutorum pulsibus uagitum pueri texerant , nunc imago rei refertur in sacris, sed pro galeis cymbala, pro scutis tympana feriuntur, ne puerum uagientem Saturnus exaudiat.`	`Candidate: quod si essent falsae notitiae enim notitias appellare tu videbare - si igitur essent eae falsae aut eius modi visis inpressae qualia visa a falsis discerni non possent, quo tandem his modo uteremur, quo modo autem quid cuique rei consentaneum esset quid repugnaret videremus?`	`0`
`Query: De quibus Uirgilius: Omnigenumque deum monstra, et latrator Anubis.`	`Candidate: haec igitur minui, cum sint detrita, videmus.`	`0`

Loss: OnlineContrastiveLoss

Training Hyperparameters

Non-Default Hyperparameters

overwrite_output_dir: True
eval_strategy: steps
per_device_train_batch_size: 32
learning_rate: 2e-05
weight_decay: 0.01
num_train_epochs: 4
warmup_steps: 448
prompts: {'query': 'Query: ', 'match': 'Candidate: '}

All Hyperparameters

Click to expand

overwrite_output_dir: True
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 32
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: 2e-05
weight_decay: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 4
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
warmup_steps: 448
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
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: False
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: False
prompts: {'query': 'Query: ', 'match': 'Candidate: '}
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Framework Versions

Python: 3.10.8
Sentence Transformers: 4.1.0
Transformers: 4.53.0
PyTorch: 2.7.1+cu126
Accelerate: 1.4.0
Datasets: 3.3.2
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",
}

	query	match	label
type	string	string	int
details	min: 10 tokens mean: 41.73 tokens max: 125 tokens	min: 6 tokens mean: 31.92 tokens max: 128 tokens	0: ~90.80% 1: ~9.20%