Add new SentenceTransformer model

2c171d7 verified 28 days ago

21.9 kB

metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:4895
  - loss:OnlineContrastiveLoss
base_model: bowphs/SPhilBerta
widget:
  - source_sentence: >-
      Query: Sed, ut ardentissimus poeta testatur, Quidquid a multis peccatur,
      inultum est: Multitudo peccantium impetrabiliorem fecit impiis veniam, ut
      qui redacti in laicos pristina sacrilegii sui debuerant scelera deplorare,
      nunc resupini in pontificali solio sedeant, et ructent nobis simulatae
      fidei nauseas, immo opertae perfidiae aperta compendia.
    sentences:
      - >-
        Candidate: Capena grandi porta qua pluit gutta   Phrygiumque Matris Almo
        qua lavat ferrum,   Horatiorum qua viret sacer campus   Et qua pusilli
        fervet Herculis fanum,   Faustine, plena Bassus ibat in raeda,   Omnis
        beati copias trahens ruris.
      - >-
        Candidate: Si qua videbuntur chartis tibi, lector, in istis   Sive
        obscura nimis sive latina parum,   Non meus est error: nocuit librarius
        illis,   Dum properat versus adnumerare tibi.
      - >-
        Candidate: ecce, nefas visu, mediis altaribus anguis  exit et extinctis
        ignibus exta rapit,  consulitur Phoebus: sors est ita reddita:
  - source_sentence: >-
      Query: ille malum uirus serpentibus addidit atris praedarique lupos
      iussit, id est odium et inuidiam et dolum hominibus inseuit, ut tam essent
      quam serpentes uenenati, tam rapaces quam lupi.
    sentences:
      - 'Candidate: quis negat?'
      - 'Candidate: quid moraris emori?'
      - 'Candidate: Et simul a medio media de parte secatur,'
  - source_sentence: >-
      Query: scintilla uigoris paterni lucet in filio et similitudo morum per
      speculum carnis erumpens: ingentes animos angusto in pectore uersat.
    sentences:
      - >-
        Candidate: quod si mihi nullum aliud esset officium in omni vita
        reliquum nisi ut erga duces ipsos et principes atque auctores salutis
        meae satis gratus iudicarer, tamen exiguum reliquae vitae tempus non
        modo ad referendam verum etiam ad commemorandam gratiam mihi relictum
        putarem.
      - >-
        Candidate: uno enim maledicto bis a me patriam servatam esse concedis,
        semel cum id feci quod omnes non negent immortalitati, si fieri potest,
        mandandum, tu supplicio puniendum putasti, iterum cum tuum multorumque
        praeter te inflammatum in bonos omnis impetum meo corpore excepi, ne eam
        civitatem quam servassem inermis armatus in discrimen adducerem.
      - >-
        Candidate: Ac siquem potuit spatiosa senectus  spectatorem operum
        multorum reddere, vixi  annos bis centum; nunc tertia vivitur aetas.
  - source_sentence: >-
      Query: si quid itaque in me potest esse consilii, si experto creditur, hoc
      primum moneo, hoc obtestor, ut sponsa Christi uinum fugiat pro ueneno.
    sentences:
      - 'Candidate: Inscripta est basis indicatque nomen.'
      - >-
        Candidate: Erras, si tibi cunnus hic videtur,   Ad quem mentula
        pertinere desit.
      - >-
        Candidate: Chthonius quoque Teleboasque  ense iacent nostro: ramum prior
        ille bifurcum  gesserat, hic iaculum; iaculo mihi vulnera fecit: 
  - source_sentence: >-
      Query: Quia ergo insanivit Israel, et percussus fornicationis spiritu,
      incredibili furore bacchatus est, ideo non multo post tempore, sed dum
      propheto, dum spiritus hos regit artus, pascet eos Dominus quasi agnum in
      latitudine.
    sentences:
      - 'Candidate: Haec omnia vidi inflammari,'
      - 'Candidate:  ut tuus amicus, Crasse, Granius non esse sextantis.'
      - >-
        Candidate: Te solum in bella secutus,  Post te fata sequar: neque enim
        sperare secunda  Fas mihi, nec liceat.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy
  - cosine_accuracy_threshold
  - cosine_f1
  - cosine_f1_threshold
  - cosine_precision
  - cosine_recall
  - cosine_ap
  - cosine_mcc
model-index:
  - name: SentenceTransformer based on bowphs/SPhilBerta
    results:
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: latin intertext
          type: latin_intertext
        metrics:
          - type: cosine_accuracy
            value: 0.9597902097902098
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.6651543378829956
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.7513227513227515
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.6328521966934204
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.8352941176470589
            name: Cosine Precision
          - type: cosine_recall
            value: 0.6826923076923077
            name: Cosine Recall
          - type: cosine_ap
            value: 0.8119318372417907
            name: Cosine Ap
          - type: cosine_mcc
            value: 0.7335872874320771
            name: Cosine Mcc

SentenceTransformer based on bowphs/SPhilBerta

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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("julian-schelb/SPhilBerta-latin-intertextuality-v1")
# Run inference
sentences = [
    'Query: Quia ergo insanivit Israel, et percussus fornicationis spiritu, incredibili furore bacchatus est, ideo non multo post tempore, sed dum propheto, dum spiritus hos regit artus, pascet eos Dominus quasi agnum in latitudine.',
    'Candidate: Te solum in bella secutus,  Post te fata sequar: neque enim sperare secunda  Fas mihi, nec liceat.',
    'Candidate:  ut tuus amicus, Crasse, Granius non esse sextantis.',
]
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]

Evaluation

Metrics

Binary Classification

Dataset: latin_intertext
Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.9598
cosine_accuracy_threshold	0.6652
cosine_f1	0.7513
cosine_f1_threshold	0.6329
cosine_precision	0.8353
cosine_recall	0.6827
cosine_ap	0.8119
cosine_mcc	0.7336

Training Details

Training Dataset

Unnamed Dataset

Size: 4,895 training samples
Columns: query, match, and label
Approximate statistics based on the first 1000 samples:
query match label
type string string int
details
min: 6 tokens
mean: 41.53 tokens
max: 128 tokens

min: 6 tokens
mean: 32.4 tokens
max: 128 tokens

0: ~91.70%
1: ~8.30%

	query	match	label
type	string	string	int
details	min: 6 tokens mean: 41.53 tokens max: 128 tokens	min: 6 tokens mean: 32.4 tokens max: 128 tokens	0: ~91.70% 1: ~8.30%

Samples:

query	match	label
`Query: quod et illustris poeta testatur dicens: sed fugit interea, fugit irreparabile tempus et iterum: Rhaebe, diu, res si qua diu mortalibus ulla est, uiximus.`	`Candidate: omnino si ego evolo mense Quintili in Graeciam, sunt omnia faciliora; sed cum sint ea tempora ut certi nihil esse possit quid honestum mihi sit, quid liceat, quid expediat, quaeso, da operam ut illum quam honestissime copiosissimeque tueamur.`	`0`
`Query: Non solum in Ecclesia morantur oves, nec mundae tantum aves volitant; sed frumentum in agro seritur, interque nitentia culta Lappaeque et tribuli, et steriles dominantur avenae.`	`Candidate: atque hoc in loco, si facultas erit, exemplis uti oportebit, quibus in simili excusatione non sit ignotum, et contentione, magis illis ignoscendum fuisse, et deliberationis partibus, turpe aut inutile esse concedi eam rem, quae ab adversario commissa sit: permagnum esse et magno futurum detrimento, si ea res ab iis, qui potestatem habent vindicandi, neglecta sit.`	`0`
`Query: adiuratus enim per eundem patrem et spes surgentis Iuli, nequaquam pepercit tums accensus et ira.`	`Candidate: factus olor niveis pendebat in aere pennis.`	`0`

Loss: OnlineContrastiveLoss

Evaluation Dataset

Unnamed Dataset

Size: 1,144 evaluation samples
Columns: query, match, and label
Approximate statistics based on the first 1000 samples:
query match label
type string string int
details
min: 8 tokens
mean: 39.04 tokens
max: 121 tokens

min: 6 tokens
mean: 32.47 tokens
max: 128 tokens

0: ~91.10%
1: ~8.90%

	query	match	label
type	string	string	int
details	min: 8 tokens mean: 39.04 tokens max: 121 tokens	min: 6 tokens mean: 32.47 tokens max: 128 tokens	0: ~91.10% 1: ~8.90%

Samples:

query	match	label
`Query: qui uero pauperes sunt et tenui substantiola uidenturque sibi scioli, pomparum ferculis similes procedunt ad publicum, ut caninam exerceant facundiam.`	`Candidate: cogitat reliquas colonias obire.`	`0`
`Query: nec uarios discet mentiri lana colores, ipse sed in pratis aries iam suaue rubenti- murice, iam croceo mutabit uellera luto, sponte sua sandyx pascentis uestiet agnos.`	`Candidate: loquitur ad voluntatem; quicquid denunciatum est, facit, assectatur, assidet, muneratur.`	`0`
`Query: credite experto, quasi Christianus Christianis loquor: uenenata sunt illius dogmata, aliena a scripturis sanctis, uim scripturis facientia.`	`Candidate: ignoscunt mihi, revocant in consuetudinem pristinam te que, quod in ea permanseris, sapientiorem quam me dicunt fuisse.`	`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: 1958
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: 1958
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

Training Logs

Epoch	Step	Training Loss	Validation Loss	latin_intertext_cosine_ap
0.6494	50	0.6022	0.1430	0.7392
1.2987	100	0.5519	0.1191	0.7579
1.9481	150	0.4728	0.1021	0.7794
2.5974	200	0.4001	0.0934	0.7917
3.2468	250	0.2689	0.0917	0.8048
3.8961	300	0.221	0.0834	0.8119

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

@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",
}