BGE SITGES CAT
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.
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
: epochper_device_train_batch_size
: 16per_device_eval_batch_size
: 16gradient_accumulation_steps
: 16learning_rate
: 2e-05num_train_epochs
: 6lr_scheduler_type
: cosinewarmup_ratio
: 0.1bf16
: Truetf32
: Falseload_best_model_at_end
: Trueoptim
: adamw_torch_fusedbatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: epochprediction_loss_only
: Trueper_device_train_batch_size
: 16per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 16eval_accumulation_steps
: Nonelearning_rate
: 2e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 6max_steps
: -1lr_scheduler_type
: cosinelr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Truefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Falselocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Trueignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torch_fusedoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falsebatch_sampler
: no_duplicatesmulti_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
@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
@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
@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}
}
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Evaluation results
- Cosine Accuracy@1 on dim 768self-reported0.073
- Cosine Accuracy@3 on dim 768self-reported0.157
- Cosine Accuracy@5 on dim 768self-reported0.218
- Cosine Accuracy@10 on dim 768self-reported0.394
- Cosine Precision@1 on dim 768self-reported0.073
- Cosine Precision@3 on dim 768self-reported0.052
- Cosine Precision@5 on dim 768self-reported0.044
- Cosine Precision@10 on dim 768self-reported0.039
- Cosine Recall@1 on dim 768self-reported0.073
- Cosine Recall@3 on dim 768self-reported0.157