SentenceTransformer based on BAAI/bge-m3
This is a sentence-transformers model finetuned from BAAI/bge-m3. 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
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/sitgrsBAAIbge-m3-290824")
# Run inference
sentences = [
'Les entitats inscrites en el Registre resten obligades a comunicar a l’Ajuntament qualsevol modificació en les seves dades registrals, podent sol·licitar la seva cancel·lació o comunicant la seva dissolució.',
"Quin és el procediment per cancel·lar la inscripció d'una entitat al Registre municipal d'entitats?",
'Quin és el paper de les entitats de protecció dels animals en la gestió de les colònies urbanes felines?',
]
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 | 0.0862 |
cosine_accuracy@3 | 0.2155 |
cosine_accuracy@5 | 0.3276 |
cosine_accuracy@10 | 0.5108 |
cosine_precision@1 | 0.0862 |
cosine_precision@3 | 0.0718 |
cosine_precision@5 | 0.0655 |
cosine_precision@10 | 0.0511 |
cosine_recall@1 | 0.0862 |
cosine_recall@3 | 0.2155 |
cosine_recall@5 | 0.3276 |
cosine_recall@10 | 0.5108 |
cosine_ndcg@10 | 0.264 |
cosine_mrr@10 | 0.1897 |
cosine_map@100 | 0.2151 |
Information Retrieval
- Dataset:
dim_768
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.0841 |
cosine_accuracy@3 | 0.2091 |
cosine_accuracy@5 | 0.319 |
cosine_accuracy@10 | 0.5 |
cosine_precision@1 | 0.0841 |
cosine_precision@3 | 0.0697 |
cosine_precision@5 | 0.0638 |
cosine_precision@10 | 0.05 |
cosine_recall@1 | 0.0841 |
cosine_recall@3 | 0.2091 |
cosine_recall@5 | 0.319 |
cosine_recall@10 | 0.5 |
cosine_ndcg@10 | 0.2595 |
cosine_mrr@10 | 0.1867 |
cosine_map@100 | 0.2132 |
Information Retrieval
- Dataset:
dim_512
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.0862 |
cosine_accuracy@3 | 0.2112 |
cosine_accuracy@5 | 0.3211 |
cosine_accuracy@10 | 0.5129 |
cosine_precision@1 | 0.0862 |
cosine_precision@3 | 0.0704 |
cosine_precision@5 | 0.0642 |
cosine_precision@10 | 0.0513 |
cosine_recall@1 | 0.0862 |
cosine_recall@3 | 0.2112 |
cosine_recall@5 | 0.3211 |
cosine_recall@10 | 0.5129 |
cosine_ndcg@10 | 0.2647 |
cosine_mrr@10 | 0.1899 |
cosine_map@100 | 0.2155 |
Information Retrieval
- Dataset:
dim_256
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.0819 |
cosine_accuracy@3 | 0.2047 |
cosine_accuracy@5 | 0.306 |
cosine_accuracy@10 | 0.5043 |
cosine_precision@1 | 0.0819 |
cosine_precision@3 | 0.0682 |
cosine_precision@5 | 0.0612 |
cosine_precision@10 | 0.0504 |
cosine_recall@1 | 0.0819 |
cosine_recall@3 | 0.2047 |
cosine_recall@5 | 0.306 |
cosine_recall@10 | 0.5043 |
cosine_ndcg@10 | 0.2555 |
cosine_mrr@10 | 0.1808 |
cosine_map@100 | 0.2066 |
Information Retrieval
- Dataset:
dim_128
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.0841 |
cosine_accuracy@3 | 0.2004 |
cosine_accuracy@5 | 0.3147 |
cosine_accuracy@10 | 0.4914 |
cosine_precision@1 | 0.0841 |
cosine_precision@3 | 0.0668 |
cosine_precision@5 | 0.0629 |
cosine_precision@10 | 0.0491 |
cosine_recall@1 | 0.0841 |
cosine_recall@3 | 0.2004 |
cosine_recall@5 | 0.3147 |
cosine_recall@10 | 0.4914 |
cosine_ndcg@10 | 0.2517 |
cosine_mrr@10 | 0.1795 |
cosine_map@100 | 0.2058 |
Information Retrieval
- Dataset:
dim_64
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.0797 |
cosine_accuracy@3 | 0.2026 |
cosine_accuracy@5 | 0.3017 |
cosine_accuracy@10 | 0.4957 |
cosine_precision@1 | 0.0797 |
cosine_precision@3 | 0.0675 |
cosine_precision@5 | 0.0603 |
cosine_precision@10 | 0.0496 |
cosine_recall@1 | 0.0797 |
cosine_recall@3 | 0.2026 |
cosine_recall@5 | 0.3017 |
cosine_recall@10 | 0.4957 |
cosine_ndcg@10 | 0.2527 |
cosine_mrr@10 | 0.1796 |
cosine_map@100 | 0.2058 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 4,173 training samples
- Columns:
positive
andanchor
- Approximate statistics based on the first 1000 samples:
positive anchor type string string details - min: 8 tokens
- mean: 48.75 tokens
- max: 125 tokens
- min: 10 tokens
- mean: 21.07 tokens
- max: 47 tokens
- Samples:
positive anchor Els ajuts per a la realització d'activitats en el lleure esportiu estan destinats a les entitats sense ànim de lucre que desenvolupen activitats esportives i de lleure.
Quins són els sectors que es beneficien dels ajuts?
En el certificat s'indiquen les dades de planejament vigent, classificació del sòl, qualificació urbanística, condicions de l’edificació i usos admesos referides a una finca o solar concreta.
Quin és el contingut de les condicions de l'edificació en el certificat d'aprofitament urbanístic?
Aportació de documentació. Ajuts per compensar la disminució d'ingressos de les empreses o establiments del sector de l'hosteleria i restauració afectats per les mesures adoptades per la situació de crisis provocada pel SARS-CoV2
Quin és el paper dels ajuts en la situació de crisis?
- 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
: 16num_train_epochs
: 10lr_scheduler_type
: cosinewarmup_ratio
: 0.2bf16
: 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
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 10max_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
: 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 | 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 |
---|---|---|---|---|---|---|---|---|
0.6130 | 10 | 3.0594 | - | - | - | - | - | - |
0.9808 | 16 | - | 0.2047 | 0.1922 | 0.2020 | 0.2016 | 0.1774 | 0.2115 |
1.2261 | 20 | 1.525 | - | - | - | - | - | - |
1.8391 | 30 | 0.7434 | - | - | - | - | - | - |
1.9617 | 32 | - | 0.2186 | 0.2003 | 0.2102 | 0.2092 | 0.1870 | 0.2101 |
2.4521 | 40 | 0.4451 | - | - | - | - | - | - |
2.9425 | 48 | - | 0.2083 | 0.2054 | 0.2091 | 0.2118 | 0.2009 | 0.2140 |
3.0651 | 50 | 0.2518 | - | - | - | - | - | - |
3.6782 | 60 | 0.1801 | - | - | - | - | - | - |
3.9847 | 65 | - | 0.2135 | 0.2071 | 0.2037 | 0.2115 | 0.2030 | 0.2191 |
4.2912 | 70 | 0.1483 | - | - | - | - | - | - |
4.9042 | 80 | 0.0893 | - | - | - | - | - | - |
4.9655 | 81 | - | 0.2066 | 0.2053 | 0.2057 | 0.2137 | 0.1982 | 0.2176 |
5.5172 | 90 | 0.0748 | - | - | - | - | - | - |
5.9464 | 97 | - | 0.2171 | 0.2113 | 0.2086 | 0.2178 | 0.2120 | 0.2193 |
6.1303 | 100 | 0.064 | - | - | - | - | - | - |
6.7433 | 110 | 0.0458 | - | - | - | - | - | - |
6.9885 | 114 | - | 0.2294 | 0.2132 | 0.2151 | 0.2227 | 0.2054 | 0.2138 |
7.3563 | 120 | 0.0436 | - | - | - | - | - | - |
7.9693 | 130 | 0.0241 | 0.2133 | 0.2083 | 0.2096 | 0.2138 | 0.2080 | 0.2124 |
8.5824 | 140 | 0.021 | - | - | - | - | - | - |
8.9502 | 146 | - | 0.216 | 0.2074 | 0.2081 | 0.2162 | 0.2094 | 0.2177 |
9.1954 | 150 | 0.0237 | - | - | - | - | - | - |
9.8084 | 160 | 0.0145 | 0.2151 | 0.2058 | 0.2066 | 0.2155 | 0.2058 | 0.2132 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.4.0+cu121
- Accelerate: 0.34.0.dev0
- Datasets: 2.21.0
- 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/sitgrsBAAIbge-m3-290824
Base model
BAAI/bge-m3
Finetuned
this model
Evaluation results
- Cosine Accuracy@1 on dim 1024self-reported0.086
- Cosine Accuracy@3 on dim 1024self-reported0.216
- Cosine Accuracy@5 on dim 1024self-reported0.328
- Cosine Accuracy@10 on dim 1024self-reported0.511
- Cosine Precision@1 on dim 1024self-reported0.086
- Cosine Precision@3 on dim 1024self-reported0.072
- Cosine Precision@5 on dim 1024self-reported0.066
- Cosine Precision@10 on dim 1024self-reported0.051
- Cosine Recall@1 on dim 1024self-reported0.086
- Cosine Recall@3 on dim 1024self-reported0.216