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/sqv-5ep")
# Run inference
sentences = [
'Import En cas de renovació per caducitat, pèrdua, sostracció o deteriorament: 12,00 € (en metàl·lic i preferiblement import exacte).',
'Quin és el procediment per a la renovació del DNI en cas de sostracció?',
"Quin és el paper del motiu legítim en l'oposició de dades personals en cas de motiu legítim i situació personal concreta?",
]
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 | 0.0407 |
cosine_accuracy@3 | 0.1174 |
cosine_accuracy@5 | 0.1815 |
cosine_accuracy@10 | 0.3302 |
cosine_precision@1 | 0.0407 |
cosine_precision@3 | 0.0391 |
cosine_precision@5 | 0.0363 |
cosine_precision@10 | 0.033 |
cosine_recall@1 | 0.0407 |
cosine_recall@3 | 0.1174 |
cosine_recall@5 | 0.1815 |
cosine_recall@10 | 0.3302 |
cosine_ndcg@10 | 0.158 |
cosine_mrr@10 | 0.1065 |
cosine_map@100 | 0.1279 |
Information Retrieval
- Dataset:
dim_768
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.0391 |
cosine_accuracy@3 | 0.108 |
cosine_accuracy@5 | 0.1815 |
cosine_accuracy@10 | 0.3286 |
cosine_precision@1 | 0.0391 |
cosine_precision@3 | 0.036 |
cosine_precision@5 | 0.0363 |
cosine_precision@10 | 0.0329 |
cosine_recall@1 | 0.0391 |
cosine_recall@3 | 0.108 |
cosine_recall@5 | 0.1815 |
cosine_recall@10 | 0.3286 |
cosine_ndcg@10 | 0.1551 |
cosine_mrr@10 | 0.1033 |
cosine_map@100 | 0.1247 |
Information Retrieval
- Dataset:
dim_512
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.0407 |
cosine_accuracy@3 | 0.1017 |
cosine_accuracy@5 | 0.1659 |
cosine_accuracy@10 | 0.3224 |
cosine_precision@1 | 0.0407 |
cosine_precision@3 | 0.0339 |
cosine_precision@5 | 0.0332 |
cosine_precision@10 | 0.0322 |
cosine_recall@1 | 0.0407 |
cosine_recall@3 | 0.1017 |
cosine_recall@5 | 0.1659 |
cosine_recall@10 | 0.3224 |
cosine_ndcg@10 | 0.1517 |
cosine_mrr@10 | 0.101 |
cosine_map@100 | 0.123 |
Information Retrieval
- Dataset:
dim_256
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.0423 |
cosine_accuracy@3 | 0.1095 |
cosine_accuracy@5 | 0.1847 |
cosine_accuracy@10 | 0.3271 |
cosine_precision@1 | 0.0423 |
cosine_precision@3 | 0.0365 |
cosine_precision@5 | 0.0369 |
cosine_precision@10 | 0.0327 |
cosine_recall@1 | 0.0423 |
cosine_recall@3 | 0.1095 |
cosine_recall@5 | 0.1847 |
cosine_recall@10 | 0.3271 |
cosine_ndcg@10 | 0.1564 |
cosine_mrr@10 | 0.1054 |
cosine_map@100 | 0.1274 |
Information Retrieval
- Dataset:
dim_128
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.0407 |
cosine_accuracy@3 | 0.1127 |
cosine_accuracy@5 | 0.18 |
cosine_accuracy@10 | 0.3146 |
cosine_precision@1 | 0.0407 |
cosine_precision@3 | 0.0376 |
cosine_precision@5 | 0.036 |
cosine_precision@10 | 0.0315 |
cosine_recall@1 | 0.0407 |
cosine_recall@3 | 0.1127 |
cosine_recall@5 | 0.18 |
cosine_recall@10 | 0.3146 |
cosine_ndcg@10 | 0.1518 |
cosine_mrr@10 | 0.1029 |
cosine_map@100 | 0.1261 |
Information Retrieval
- Dataset:
dim_64
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.0407 |
cosine_accuracy@3 | 0.0986 |
cosine_accuracy@5 | 0.1596 |
cosine_accuracy@10 | 0.2911 |
cosine_precision@1 | 0.0407 |
cosine_precision@3 | 0.0329 |
cosine_precision@5 | 0.0319 |
cosine_precision@10 | 0.0291 |
cosine_recall@1 | 0.0407 |
cosine_recall@3 | 0.0986 |
cosine_recall@5 | 0.1596 |
cosine_recall@10 | 0.2911 |
cosine_ndcg@10 | 0.1405 |
cosine_mrr@10 | 0.0955 |
cosine_map@100 | 0.1194 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 5,750 training samples
- Columns:
positive
andanchor
- Approximate statistics based on the first 1000 samples:
positive anchor type string string details - min: 4 tokens
- mean: 43.32 tokens
- max: 128 tokens
- min: 9 tokens
- mean: 20.77 tokens
- max: 45 tokens
- Samples:
positive anchor Aquest tràmit permet donar d'alta ofertes de treball que es gestionaran pel Servei a l'Ocupació.
Com puc saber si el meu perfil és compatible amb les ofertes de treball?
El titular de l’activitat ha de declarar sota la seva responsabilitat, que compleix els requisits establerts per la normativa vigent per a l’exercici de l’activitat, que disposa d’un certificat tècnic justificatiu i que es compromet a mantenir-ne el compliment durant el seu exercici.
Quin és el paper del titular de l'activitat en la Declaració responsable?
Aquest tipus de transmissió entre cedent i cessionari només podrà ser de caràcter gratuït i no condicionada.
Quin és el paper del cedent en la transmissió de drets funeraris?
- 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
: 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
: 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 |
---|---|---|---|---|---|---|---|---|
0.4444 | 10 | 4.5093 | - | - | - | - | - | - |
0.8889 | 20 | 2.7989 | - | - | - | - | - | - |
0.9778 | 22 | - | 0.1072 | 0.1182 | 0.1122 | 0.1083 | 0.1044 | 0.1082 |
1.3333 | 30 | 1.8343 | - | - | - | - | - | - |
1.7778 | 40 | 1.5248 | - | - | - | - | - | - |
2.0 | 45 | - | 0.1182 | 0.1203 | 0.1163 | 0.1188 | 0.1209 | 0.1229 |
2.2222 | 50 | 0.9624 | - | - | - | - | - | - |
2.6667 | 60 | 1.1161 | - | - | - | - | - | - |
2.9778 | 67 | - | 0.1235 | 0.1324 | 0.1302 | 0.1252 | 0.1213 | 0.1239 |
3.1111 | 70 | 0.7405 | - | - | - | - | - | - |
3.5556 | 80 | 0.8621 | - | - | - | - | - | - |
4.0 | 90 | 0.6071 | 0.1249 | 0.1282 | 0.1310 | 0.1280 | 0.1181 | 0.1278 |
4.4444 | 100 | 0.7091 | - | - | - | - | - | - |
4.8889 | 110 | 0.606 | 0.1279 | 0.1261 | 0.1274 | 0.1230 | 0.1194 | 0.1247 |
- 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: 0.35.0.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/sqv-5ep
Base model
BAAI/bge-m3
Finetuned
this model
Evaluation results
- Cosine Accuracy@1 on dim 1024self-reported0.041
- Cosine Accuracy@3 on dim 1024self-reported0.117
- Cosine Accuracy@5 on dim 1024self-reported0.182
- Cosine Accuracy@10 on dim 1024self-reported0.330
- Cosine Precision@1 on dim 1024self-reported0.041
- Cosine Precision@3 on dim 1024self-reported0.039
- Cosine Precision@5 on dim 1024self-reported0.036
- Cosine Precision@10 on dim 1024self-reported0.033
- Cosine Recall@1 on dim 1024self-reported0.041
- Cosine Recall@3 on dim 1024self-reported0.117