SentenceTransformer based on BAAI/bge-m3
This is a sentence-transformers model finetuned from BAAI/bge-m3 on the json dataset. It maps sentences & paragraphs to a 1024-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: BAAI/bge-m3
- Maximum Sequence Length: 8192 tokens
- Output Dimensionality: 1024 tokens
- Similarity Function: Cosine Similarity
- Training Dataset:
- json
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': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, '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): 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.
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("adriansanz/sqv2")
# Run inference
sentences = [
'La presentació de la sol·licitud no dona dret al muntatge de la parada.',
'Quin és el requisit per a la presentació de la sol·licitud d’autorització?',
'Quin és el motiu per canviar la persona titular dels drets funeraris?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
Information Retrieval
- Dataset:
dim_1024
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.044 |
cosine_accuracy@3 | 0.116 |
cosine_accuracy@5 | 0.18 |
cosine_accuracy@10 | 0.3507 |
cosine_precision@1 | 0.044 |
cosine_precision@3 | 0.0387 |
cosine_precision@5 | 0.036 |
cosine_precision@10 | 0.0351 |
cosine_recall@1 | 0.044 |
cosine_recall@3 | 0.116 |
cosine_recall@5 | 0.18 |
cosine_recall@10 | 0.3507 |
cosine_ndcg@10 | 0.1659 |
cosine_mrr@10 | 0.111 |
cosine_map@100 | 0.1341 |
Information Retrieval
- Dataset:
dim_768
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.0413 |
cosine_accuracy@3 | 0.116 |
cosine_accuracy@5 | 0.1787 |
cosine_accuracy@10 | 0.3627 |
cosine_precision@1 | 0.0413 |
cosine_precision@3 | 0.0387 |
cosine_precision@5 | 0.0357 |
cosine_precision@10 | 0.0363 |
cosine_recall@1 | 0.0413 |
cosine_recall@3 | 0.116 |
cosine_recall@5 | 0.1787 |
cosine_recall@10 | 0.3627 |
cosine_ndcg@10 | 0.169 |
cosine_mrr@10 | 0.1116 |
cosine_map@100 | 0.1341 |
Information Retrieval
- Dataset:
dim_512
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.0467 |
cosine_accuracy@3 | 0.116 |
cosine_accuracy@5 | 0.1787 |
cosine_accuracy@10 | 0.356 |
cosine_precision@1 | 0.0467 |
cosine_precision@3 | 0.0387 |
cosine_precision@5 | 0.0357 |
cosine_precision@10 | 0.0356 |
cosine_recall@1 | 0.0467 |
cosine_recall@3 | 0.116 |
cosine_recall@5 | 0.1787 |
cosine_recall@10 | 0.356 |
cosine_ndcg@10 | 0.1677 |
cosine_mrr@10 | 0.1121 |
cosine_map@100 | 0.1346 |
Information Retrieval
- Dataset:
dim_256
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.0387 |
cosine_accuracy@3 | 0.1067 |
cosine_accuracy@5 | 0.1707 |
cosine_accuracy@10 | 0.3413 |
cosine_precision@1 | 0.0387 |
cosine_precision@3 | 0.0356 |
cosine_precision@5 | 0.0341 |
cosine_precision@10 | 0.0341 |
cosine_recall@1 | 0.0387 |
cosine_recall@3 | 0.1067 |
cosine_recall@5 | 0.1707 |
cosine_recall@10 | 0.3413 |
cosine_ndcg@10 | 0.1587 |
cosine_mrr@10 | 0.1046 |
cosine_map@100 | 0.129 |
Information Retrieval
- Dataset:
dim_128
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.0493 |
cosine_accuracy@3 | 0.1227 |
cosine_accuracy@5 | 0.1987 |
cosine_accuracy@10 | 0.3667 |
cosine_precision@1 | 0.0493 |
cosine_precision@3 | 0.0409 |
cosine_precision@5 | 0.0397 |
cosine_precision@10 | 0.0367 |
cosine_recall@1 | 0.0493 |
cosine_recall@3 | 0.1227 |
cosine_recall@5 | 0.1987 |
cosine_recall@10 | 0.3667 |
cosine_ndcg@10 | 0.1759 |
cosine_mrr@10 | 0.119 |
cosine_map@100 | 0.142 |
Information Retrieval
- Dataset:
dim_64
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.0373 |
cosine_accuracy@3 | 0.0947 |
cosine_accuracy@5 | 0.1573 |
cosine_accuracy@10 | 0.34 |
cosine_precision@1 | 0.0373 |
cosine_precision@3 | 0.0316 |
cosine_precision@5 | 0.0315 |
cosine_precision@10 | 0.034 |
cosine_recall@1 | 0.0373 |
cosine_recall@3 | 0.0947 |
cosine_recall@5 | 0.1573 |
cosine_recall@10 | 0.34 |
cosine_ndcg@10 | 0.1535 |
cosine_mrr@10 | 0.0987 |
cosine_map@100 | 0.1226 |
Training Details
Training Dataset
json
- Dataset: json
- Size: 6,749 training samples
- Columns:
positive
andanchor
- Approximate statistics based on the first 1000 samples:
positive anchor type string string details - min: 6 tokens
- mean: 42.03 tokens
- max: 106 tokens
- min: 10 tokens
- mean: 20.32 tokens
- max: 54 tokens
- Samples:
positive anchor Aquest tràmit us permet compensar deutes de naturalesa pública a favor de l'Ajuntament, sigui quin sigui el seu estat (voluntari/executiu), amb crèdits reconeguts per aquest a favor del mateix deutor, i que el seu estat sigui pendent de pagament.
Quin és el benefici de la compensació de deutes amb crèdits?
El seu objecte és que -prèviament a la seva execució material- l'Ajuntament comprovi l'adequació de l’actuació a la normativa i planejament, així com a les ordenances municipals sobre l’ús del sòl i edificació.
Quin és el paper de les ordenances municipals en aquest tràmit?
Comunicació prèvia del manteniment en espais, zones o instal·lacions comunitàries interiors dels edificis (reparació i/o millora de materials).
Quin és el límit del manteniment en espais comunitaris interiors dels edificis?
- Loss:
MatryoshkaLoss
with these parameters:{ "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 }
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
: 5lr_scheduler_type
: cosinewarmup_ratio
: 0.2bf16
: Truetf32
: Trueload_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
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 2e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 5max_steps
: -1lr_scheduler_type
: cosinelr_scheduler_kwargs
: {}warmup_ratio
: 0.2warmup_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
: Truelocal_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
: Falseeval_use_gather_object
: Falsebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | dim_1024_cosine_map@100 | 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 |
---|---|---|---|---|---|---|---|---|
0.3791 | 10 | 3.0867 | - | - | - | - | - | - |
0.7583 | 20 | 2.4414 | - | - | - | - | - | - |
0.9858 | 26 | - | 0.1266 | 0.1255 | 0.1232 | 0.1257 | 0.1091 | 0.1345 |
1.1351 | 30 | 1.7091 | - | - | - | - | - | - |
1.5142 | 40 | 1.2495 | - | - | - | - | - | - |
1.8934 | 50 | 0.9813 | - | - | - | - | - | - |
1.9692 | 52 | - | 0.1315 | 0.1325 | 0.1285 | 0.1328 | 0.1218 | 0.1309 |
2.2701 | 60 | 0.6918 | - | - | - | - | - | - |
2.6493 | 70 | 0.7146 | - | - | - | - | - | - |
2.9905 | 79 | - | 0.1370 | 0.1344 | 0.1355 | 0.1338 | 0.1269 | 0.1363 |
3.0261 | 80 | 0.6002 | - | - | - | - | - | - |
3.4052 | 90 | 0.4816 | - | - | - | - | - | - |
3.7844 | 100 | 0.4949 | - | - | - | - | - | - |
3.9739 | 105 | - | 0.1357 | 0.1393 | 0.1302 | 0.1347 | 0.1204 | 0.1354 |
4.1611 | 110 | 0.474 | - | - | - | - | - | - |
4.5403 | 120 | 0.4692 | - | - | - | - | - | - |
4.9194 | 130 | 0.4484 | 0.1341 | 0.142 | 0.129 | 0.1346 | 0.1226 | 0.1341 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.0
- Transformers: 4.44.2
- PyTorch: 2.4.0+cu121
- Accelerate: 0.35.0.dev0
- Datasets: 3.0.0
- 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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Model tree for adriansanz/sqv2
Base model
BAAI/bge-m3
Finetuned
this model
Evaluation results
- Cosine Accuracy@1 on dim 1024self-reported0.044
- Cosine Accuracy@3 on dim 1024self-reported0.116
- Cosine Accuracy@5 on dim 1024self-reported0.180
- Cosine Accuracy@10 on dim 1024self-reported0.351
- Cosine Precision@1 on dim 1024self-reported0.044
- Cosine Precision@3 on dim 1024self-reported0.039
- Cosine Precision@5 on dim 1024self-reported0.036
- Cosine Precision@10 on dim 1024self-reported0.035
- Cosine Recall@1 on dim 1024self-reported0.044
- Cosine Recall@3 on dim 1024self-reported0.116