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BGE SITGES CAT

Name: SITGES-aina4_v2
Availability: InStock

This is a sentence-transformers model finetuned from projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base. 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: projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base
Maximum Sequence Length: 128 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity

Language: ca
License: apache-2.0

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: XLMRobertaModel 
  (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("adriansanz/SITGES-aina4")
# Run inference
sentences = [
    "Mitjançant aquest tràmit la persona interessada posa en coneixement de l'Ajuntament de Sitges l'inici d'un espectacle públic o activitat recreativa de caràcter extraordinari...",
    'Quin és el paper de la persona interessada en la llicència per a espectacles públics o activitats recreatives de caràcter extraordinari?',
    "Quin és el paper del Registre de Sol·licitants d'Habitatge amb Protecció Oficial en la gestió d'habitatges?",
]
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

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0733
cosine_accuracy@3	0.1573
cosine_accuracy@5	0.2177
cosine_accuracy@10	0.3944
cosine_precision@1	0.0733
cosine_precision@3	0.0524
cosine_precision@5	0.0435
cosine_precision@10	0.0394
cosine_recall@1	0.0733
cosine_recall@3	0.1573
cosine_recall@5	0.2177
cosine_recall@10	0.3944
cosine_ndcg@10	0.2013
cosine_mrr@10	0.1439
cosine_map@100	0.171

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0733
cosine_accuracy@3	0.1509
cosine_accuracy@5	0.2177
cosine_accuracy@10	0.3944
cosine_precision@1	0.0733
cosine_precision@3	0.0503
cosine_precision@5	0.0435
cosine_precision@10	0.0394
cosine_recall@1	0.0733
cosine_recall@3	0.1509
cosine_recall@5	0.2177
cosine_recall@10	0.3944
cosine_ndcg@10	0.2016
cosine_mrr@10	0.1444
cosine_map@100	0.1716

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0733
cosine_accuracy@3	0.1487
cosine_accuracy@5	0.2112
cosine_accuracy@10	0.4009
cosine_precision@1	0.0733
cosine_precision@3	0.0496
cosine_precision@5	0.0422
cosine_precision@10	0.0401
cosine_recall@1	0.0733
cosine_recall@3	0.1487
cosine_recall@5	0.2112
cosine_recall@10	0.4009
cosine_ndcg@10	0.2021
cosine_mrr@10	0.1434
cosine_map@100	0.1697

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.069
cosine_accuracy@3	0.1466
cosine_accuracy@5	0.2177
cosine_accuracy@10	0.3815
cosine_precision@1	0.069
cosine_precision@3	0.0489
cosine_precision@5	0.0435
cosine_precision@10	0.0381
cosine_recall@1	0.069
cosine_recall@3	0.1466
cosine_recall@5	0.2177
cosine_recall@10	0.3815
cosine_ndcg@10	0.1954
cosine_mrr@10	0.1398
cosine_map@100	0.166

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.056
cosine_accuracy@3	0.1379
cosine_accuracy@5	0.194
cosine_accuracy@10	0.3685
cosine_precision@1	0.056
cosine_precision@3	0.046
cosine_precision@5	0.0388
cosine_precision@10	0.0369
cosine_recall@1	0.056
cosine_recall@3	0.1379
cosine_recall@5	0.194
cosine_recall@10	0.3685
cosine_ndcg@10	0.1823
cosine_mrr@10	0.1269
cosine_map@100	0.1543

Training Details

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
gradient_accumulation_steps: 16
learning_rate: 2e-05
num_train_epochs: 6
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: True
tf32: False
load_best_model_at_end: True
optim: adamw_torch_fused
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 16
eval_accumulation_steps: None
learning_rate: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 6
max_steps: -1
lr_scheduler_type: cosine
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
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: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: False
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: True
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_fused
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: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
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
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	dim_128_cosine_map@100	dim_256_cosine_map@100	dim_512_cosine_map@100	dim_64_cosine_map@100	dim_768_cosine_map@100
0.3065	5	3.3947	-	-	-	-	-	-
0.6130	10	2.6401	-	-	-	-	-	-
0.9195	15	2.0152	-	-	-	-	-	-
0.9808	16	-	1.3404	0.1639	0.1577	0.1694	0.1503	0.1638
1.2261	20	1.4542	-	-	-	-	-	-
1.5326	25	1.0135	-	-	-	-	-	-
1.8391	30	0.8437	-	-	-	-	-	-
1.9617	32	-	0.9436	0.1556	0.1596	0.1600	0.1467	0.1701
2.1456	35	0.7676	-	-	-	-	-	-
2.4521	40	0.5126	-	-	-	-	-	-
2.7586	45	0.4358	-	-	-	-	-	-
2.9425	48	-	0.7852	0.1650	0.1693	0.1720	0.1511	0.1686
3.0651	50	0.4192	-	-	-	-	-	-
3.3716	55	0.3429	-	-	-	-	-	-
3.6782	60	0.3025	-	-	-	-	-	-
3.9847	65	0.2863	0.7401	0.1646	0.1706	0.1759	0.1480	0.1694
4.2912	70	0.2474	-	-	-	-	-	-
4.5977	75	0.2324	-	-	-	-	-	-
4.9042	80	0.2344	-	-	-	-	-	-
4.9655	81	-	0.7217	0.1663	0.1699	0.1767	0.1512	0.1696
5.2107	85	0.2181	-	-	-	-	-	-
5.5172	90	0.2116	-	-	-	-	-	-
5.8238	95	0.1926	-	-	-	-	-	-
5.8851	96	-	0.7154	0.166	0.1697	0.1716	0.1543	0.171

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.42.3
PyTorch: 2.3.1+cu121
Accelerate: 0.32.1
Datasets: 2.20.0
Tokenizers: 0.19.1

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",
}

MatryoshkaLoss

bibtex

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning}, 
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

bibtex

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

adriansanz/SITGES-aina4_v2

BGE SITGES CAT

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Information Retrieval

Information Retrieval

Information Retrieval

Information Retrieval

Information Retrieval

Training Details

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

MatryoshkaLoss

MultipleNegativesRankingLoss

adriansanz/
SITGES-aina4_v2