SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-mpnet-base-v2 on the allstats-semantic-search-synthetic-dataset-v1 dataset. 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: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
- Maximum Sequence Length: 128 tokens
- Output Dimensionality: 768 dimensions
- Similarity Function: Cosine Similarity
- Training Dataset:
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("yahyaabd/allstats-semantic-search-model-v1-3-2")
# Run inference
sentences = [
'perubahan nilai tukar petani bulan mei 2017',
'Perkembangan Nilai Tukar Petani Mei 2017',
'Statistik Restoran/Rumah Makan Tahun 2014',
]
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
Semantic Similarity
- Datasets:
allstats-semantic-search-v1-3-dev
andallstat-semantic-search-v1-3-test
- Evaluated with
EmbeddingSimilarityEvaluator
Metric | allstats-semantic-search-v1-3-dev | allstat-semantic-search-v1-3-test |
---|---|---|
pearson_cosine | 0.9959 | 0.9961 |
spearman_cosine | 0.9641 | 0.9648 |
Training Details
Training Dataset
allstats-semantic-search-synthetic-dataset-v1
- Dataset: allstats-semantic-search-synthetic-dataset-v1 at b13c0a7
- Size: 212,940 training samples
- Columns:
query
,doc
, andlabel
- Approximate statistics based on the first 1000 samples:
query doc label type string string float details - min: 5 tokens
- mean: 11.46 tokens
- max: 34 tokens
- min: 5 tokens
- mean: 14.47 tokens
- max: 54 tokens
- min: 0.0
- mean: 0.5
- max: 1.05
- Samples:
query doc label aDta industri besar dan sedang Indonesia 2008
Statistik Industri Besar dan Sedang Indonesia 2008
0.9
profil bisnis konstruksi individu jawa barat 2022
Statistik Industri Manufaktur Indonesia 2015 - Bahan Baku
0.15
data statistik ekonomi indonesia
Nilai Tukar Valuta Asing di Indonesia 2014
0.08
- Loss:
CosineSimilarityLoss
with these parameters:{ "loss_fct": "torch.nn.modules.loss.MSELoss" }
Evaluation Dataset
allstats-semantic-search-synthetic-dataset-v1
- Dataset: allstats-semantic-search-synthetic-dataset-v1 at b13c0a7
- Size: 26,618 evaluation samples
- Columns:
query
,doc
, andlabel
- Approximate statistics based on the first 1000 samples:
query doc label type string string float details - min: 5 tokens
- mean: 11.38 tokens
- max: 34 tokens
- min: 4 tokens
- mean: 14.63 tokens
- max: 55 tokens
- min: 0.0
- mean: 0.51
- max: 1.0
- Samples:
query doc label tahun berapa ekspor naik 2,37% dan impor naik 30,30%?
Bulan November 2006 Ekspor Naik 2,37 % dan Impor Naik 30,30 %
1.0
Berapa produksi padi pada tahun 2023?
Produksi padi tahun lainnya
0.0
data statistik solus per aqua 2015
Statistik Solus Per Aqua (SPA) 2015
0.97
- Loss:
CosineSimilarityLoss
with these parameters:{ "loss_fct": "torch.nn.modules.loss.MSELoss" }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 64per_device_eval_batch_size
: 64num_train_epochs
: 16warmup_ratio
: 0.1fp16
: True
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 64per_device_eval_batch_size
: 64per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 16max_steps
: -1lr_scheduler_type
: linearlr_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
: Falsefp16
: Truefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_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
: Falseignore_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_torchoptim_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
: Nonehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseinclude_for_metrics
: []eval_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
: Falseuse_liger_kernel
: Falseeval_use_gather_object
: Falseaverage_tokens_across_devices
: Falseprompts
: Nonebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: proportional
Training Logs
Click to expand
Epoch | Step | Training Loss | Validation Loss | allstats-semantic-search-v1-3-dev_spearman_cosine | allstat-semantic-search-v1-3-test_spearman_cosine |
---|---|---|---|---|---|
0.1502 | 500 | 0.0579 | 0.0351 | 0.7132 | - |
0.3005 | 1000 | 0.03 | 0.0225 | 0.7589 | - |
0.4507 | 1500 | 0.0219 | 0.0185 | 0.7834 | - |
0.6010 | 2000 | 0.0181 | 0.0163 | 0.7946 | - |
0.7512 | 2500 | 0.0162 | 0.0147 | 0.7941 | - |
0.9014 | 3000 | 0.015 | 0.0147 | 0.8050 | - |
1.0517 | 3500 | 0.014 | 0.0131 | 0.7946 | - |
1.2019 | 4000 | 0.0119 | 0.0126 | 0.8038 | - |
1.3522 | 4500 | 0.0121 | 0.0128 | 0.8213 | - |
1.5024 | 5000 | 0.0117 | 0.0116 | 0.8268 | - |
1.6526 | 5500 | 0.0124 | 0.0117 | 0.8269 | - |
1.8029 | 6000 | 0.0111 | 0.0109 | 0.8421 | - |
1.9531 | 6500 | 0.0105 | 0.0108 | 0.8278 | - |
2.1034 | 7000 | 0.0091 | 0.0093 | 0.8460 | - |
2.2536 | 7500 | 0.0085 | 0.0091 | 0.8469 | - |
2.4038 | 8000 | 0.0079 | 0.0083 | 0.8595 | - |
2.5541 | 8500 | 0.0075 | 0.0085 | 0.8495 | - |
2.7043 | 9000 | 0.0073 | 0.0082 | 0.8614 | - |
2.8546 | 9500 | 0.0068 | 0.0077 | 0.8696 | - |
3.0048 | 10000 | 0.0066 | 0.0076 | 0.8669 | - |
3.1550 | 10500 | 0.0058 | 0.0072 | 0.8678 | - |
3.3053 | 11000 | 0.0056 | 0.0067 | 0.8703 | - |
3.4555 | 11500 | 0.0054 | 0.0067 | 0.8766 | - |
3.6058 | 12000 | 0.0054 | 0.0063 | 0.8678 | - |
3.7560 | 12500 | 0.0051 | 0.0061 | 0.8786 | - |
3.9062 | 13000 | 0.0052 | 0.0077 | 0.8699 | - |
4.0565 | 13500 | 0.005 | 0.0055 | 0.8859 | - |
4.2067 | 14000 | 0.0041 | 0.0054 | 0.8900 | - |
4.3570 | 14500 | 0.0038 | 0.0052 | 0.8892 | - |
4.5072 | 15000 | 0.0039 | 0.0050 | 0.8895 | - |
4.6575 | 15500 | 0.004 | 0.0052 | 0.8972 | - |
4.8077 | 16000 | 0.0042 | 0.0051 | 0.8927 | - |
4.9579 | 16500 | 0.0041 | 0.0052 | 0.8930 | - |
5.1082 | 17000 | 0.0034 | 0.0053 | 0.8998 | - |
5.2584 | 17500 | 0.003 | 0.0047 | 0.9023 | - |
5.4087 | 18000 | 0.0032 | 0.0045 | 0.9039 | - |
5.5589 | 18500 | 0.0032 | 0.0044 | 0.8996 | - |
5.7091 | 19000 | 0.0032 | 0.0041 | 0.9085 | - |
5.8594 | 19500 | 0.0032 | 0.0047 | 0.9072 | - |
6.0096 | 20000 | 0.0029 | 0.0037 | 0.9104 | - |
6.1599 | 20500 | 0.0024 | 0.0037 | 0.9112 | - |
6.3101 | 21000 | 0.0026 | 0.0039 | 0.9112 | - |
6.4603 | 21500 | 0.0024 | 0.0037 | 0.9157 | - |
6.6106 | 22000 | 0.0022 | 0.0038 | 0.9122 | - |
6.7608 | 22500 | 0.0025 | 0.0034 | 0.9170 | - |
6.9111 | 23000 | 0.0023 | 0.0034 | 0.9179 | - |
7.0613 | 23500 | 0.002 | 0.0031 | 0.9244 | - |
7.2115 | 24000 | 0.0019 | 0.0030 | 0.9250 | - |
7.3618 | 24500 | 0.0018 | 0.0032 | 0.9249 | - |
7.5120 | 25000 | 0.0022 | 0.0031 | 0.9162 | - |
7.6623 | 25500 | 0.0019 | 0.0030 | 0.9266 | - |
7.8125 | 26000 | 0.0019 | 0.0028 | 0.9297 | - |
7.9627 | 26500 | 0.0018 | 0.0028 | 0.9282 | - |
8.1130 | 27000 | 0.0015 | 0.0025 | 0.9324 | - |
8.2632 | 27500 | 0.0014 | 0.0027 | 0.9337 | - |
8.4135 | 28000 | 0.0015 | 0.0027 | 0.9327 | - |
8.5637 | 28500 | 0.0016 | 0.0027 | 0.9313 | - |
8.7139 | 29000 | 0.0016 | 0.0027 | 0.9333 | - |
8.8642 | 29500 | 0.0015 | 0.0025 | 0.9382 | - |
9.0144 | 30000 | 0.0014 | 0.0025 | 0.9375 | - |
9.1647 | 30500 | 0.0011 | 0.0024 | 0.9398 | - |
9.3149 | 31000 | 0.0012 | 0.0025 | 0.9384 | - |
9.4651 | 31500 | 0.0014 | 0.0025 | 0.9383 | - |
9.6154 | 32000 | 0.0013 | 0.0023 | 0.9410 | - |
9.7656 | 32500 | 0.0011 | 0.0023 | 0.9409 | - |
9.9159 | 33000 | 0.0012 | 0.0021 | 0.9432 | - |
10.0661 | 33500 | 0.0011 | 0.0021 | 0.9432 | - |
10.2163 | 34000 | 0.001 | 0.0021 | 0.9442 | - |
10.3666 | 34500 | 0.0009 | 0.0022 | 0.9436 | - |
10.5168 | 35000 | 0.001 | 0.0021 | 0.9468 | - |
10.6671 | 35500 | 0.001 | 0.0020 | 0.9471 | - |
10.8173 | 36000 | 0.001 | 0.0021 | 0.9467 | - |
10.9675 | 36500 | 0.0011 | 0.0021 | 0.9478 | - |
11.1178 | 37000 | 0.0008 | 0.0020 | 0.9493 | - |
11.2680 | 37500 | 0.0008 | 0.0019 | 0.9509 | - |
11.4183 | 38000 | 0.0008 | 0.0019 | 0.9504 | - |
11.5685 | 38500 | 0.0008 | 0.0019 | 0.9512 | - |
11.7188 | 39000 | 0.0008 | 0.0019 | 0.9516 | - |
11.8690 | 39500 | 0.0007 | 0.0019 | 0.9534 | - |
12.0192 | 40000 | 0.0007 | 0.0018 | 0.9539 | - |
12.1695 | 40500 | 0.0006 | 0.0018 | 0.9555 | - |
12.3197 | 41000 | 0.0006 | 0.0019 | 0.9551 | - |
12.4700 | 41500 | 0.0007 | 0.0019 | 0.9550 | - |
12.6202 | 42000 | 0.0008 | 0.0018 | 0.9552 | - |
12.7704 | 42500 | 0.0006 | 0.0017 | 0.9559 | - |
12.9207 | 43000 | 0.0006 | 0.0017 | 0.9568 | - |
13.0709 | 43500 | 0.0006 | 0.0017 | 0.9577 | - |
13.2212 | 44000 | 0.0005 | 0.0017 | 0.9581 | - |
13.3714 | 44500 | 0.0006 | 0.0017 | 0.9586 | - |
13.5216 | 45000 | 0.0005 | 0.0017 | 0.9587 | - |
13.6719 | 45500 | 0.0005 | 0.0017 | 0.9591 | - |
13.8221 | 46000 | 0.0006 | 0.0016 | 0.9600 | - |
13.9724 | 46500 | 0.0005 | 0.0016 | 0.9603 | - |
14.1226 | 47000 | 0.0005 | 0.0016 | 0.9609 | - |
14.2728 | 47500 | 0.0005 | 0.0016 | 0.9612 | - |
14.4231 | 48000 | 0.0005 | 0.0016 | 0.9611 | - |
14.5733 | 48500 | 0.0005 | 0.0016 | 0.9616 | - |
14.7236 | 49000 | 0.0004 | 0.0015 | 0.9625 | - |
14.8738 | 49500 | 0.0004 | 0.0016 | 0.9628 | - |
15.0240 | 50000 | 0.0004 | 0.0016 | 0.9631 | - |
15.1743 | 50500 | 0.0004 | 0.0016 | 0.9632 | - |
15.3245 | 51000 | 0.0004 | 0.0016 | 0.9633 | - |
15.4748 | 51500 | 0.0004 | 0.0016 | 0.9635 | - |
15.625 | 52000 | 0.0004 | 0.0015 | 0.9638 | - |
15.7752 | 52500 | 0.0004 | 0.0015 | 0.9640 | - |
15.9255 | 53000 | 0.0004 | 0.0015 | 0.9641 | - |
16.0 | 53248 | - | - | - | 0.9648 |
Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.2.2+cu121
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0
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",
}
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Dataset used to train yahyaabd/allstats-semantic-search-model-v1-3-2
Evaluation results
- Pearson Cosine on allstats semantic search v1 3 devself-reported0.996
- Spearman Cosine on allstats semantic search v1 3 devself-reported0.964
- Pearson Cosine on allstat semantic search v1 3 testself-reported0.996
- Spearman Cosine on allstat semantic search v1 3 testself-reported0.965