yahyaabd/test-model

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-search-pairs-dataset 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:
  • allstats-search-pairs-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/test-model")
# Run inference
sentences = [
    'Biaya hidup kelompok perumahan Indonesia 2017',
    'Statistik Upah 2013',
    'Survei Biaya Hidup (SBH) 2018 Bulukumba, Watampone, Makassar, Pare-Pare, dan Palopo',
]
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-mpnet-eval and allstats-semantic-mpnet-test
• Evaluated with EmbeddingSimilarityEvaluator

Metric	allstats-semantic-mpnet-eval	allstats-semantic-mpnet-test
pearson_cosine	0.9831	0.9832
spearman_cosine	0.841	0.8379

Training Details

Training Dataset

allstats-search-pairs-dataset

• Dataset: allstats-search-pairs-dataset at 6712cb1
• Size: 79,621 training samples
• Columns: query, doc, and label
• Approximate statistics based on the first 1000 samples:

  	query	doc	label
  type	string	string	float
  details	min: 5 tokens mean: 10.78 tokens max: 39 tokens	min: 5 tokens mean: 13.73 tokens max: 58 tokens	min: 0.0 mean: 0.44 max: 0.99

• Samples:

  query	doc	label
  Produksi jagung di Indonesia tahun 2009	Indeks Unit Value Ekspor Menurut Kode SITC Bulan Februari 2024	0.1
  Data produksi industri manufaktur 2021	Perkembangan Indeks Produksi Industri Manufaktur 2021	0.96
  direktori perusahaan industri penggilingan padi tahun 2012 provinsi sulawesi utara dan gorontalo	Neraca Pemerintahan Umum Indonesia 2007-2012	0.03

• Loss: CosineSimilarityLoss with these parameters:

  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  

Evaluation Dataset

allstats-search-pairs-dataset

• Dataset: allstats-search-pairs-dataset at 6712cb1
• Size: 9,952 evaluation samples
• Columns: query, doc, and label
• Approximate statistics based on the first 1000 samples:

  	query	doc	label
  type	string	string	float
  details	min: 5 tokens mean: 10.75 tokens max: 40 tokens	min: 4 tokens mean: 14.09 tokens max: 49 tokens	min: 0.01 mean: 0.48 max: 0.99

• Samples:

  query	doc	label
  Daftar perusahaan industri pengolahan skala kecil 2006	Statistik Migrasi Nusa Tenggara Barat Hasil SP 2010	0.05
  Populasi Indonesia per provinsi 2000-2010	Indikator Ekonomi Desember 2023	0.08
  Data harga barang desa non-pangan tahun 2022	Statistik Kunjungan Tamu Asing 2004	0.1

• Loss: CosineSimilarityLoss with these parameters:

  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 32
• num_train_epochs: 6
• warmup_ratio: 0.1
• fp16: True
• dataloader_num_workers: 4
• load_best_model_at_end: True
• label_smoothing_factor: 0.01
• eval_on_start: True

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 32
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 5e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 6
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 42
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: False
• fp16: True
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 0
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 4
• dataloader_prefetch_factor: None
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: True
• ignore_data_skip: False
• fsdp: []
• fsdp_min_num_params: 0
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• fsdp_transformer_layer_cls_to_wrap: None
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• deepspeed: None
• label_smoothing_factor: 0.01
• optim: adamw_torch
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• skip_memory_metrics: True
• use_legacy_prediction_loop: False
• push_to_hub: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: None
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• include_for_metrics: []
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• dispatch_batches: None
• split_batches: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: True
• use_liger_kernel: False
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	allstats-semantic-mpnet-eval_spearman_cosine	allstats-semantic-mpnet-test_spearman_cosine
0	0	-	0.0958	0.6404	-
0.1004	250	0.0482	0.0269	0.7685	-
0.2009	500	0.0249	0.0200	0.7737	-
0.3013	750	0.0196	0.0172	0.7768	-
0.4018	1000	0.0184	0.0172	0.7744	-
0.5022	1250	0.0185	0.0159	0.7751	-
0.6027	1500	0.0155	0.0156	0.7825	-
0.7031	1750	0.0165	0.0149	0.7826	-
0.8035	2000	0.0146	0.0136	0.7791	-
0.9040	2250	0.0132	0.0127	0.7825	-
1.0044	2500	0.0127	0.0129	0.7818	-
1.1049	2750	0.0104	0.0114	0.7849	-
1.2053	3000	0.009	0.0108	0.7870	-
1.3057	3250	0.0091	0.0112	0.7877	-
1.4062	3500	0.009	0.0104	0.7888	-
1.5066	3750	0.009	0.0101	0.7937	-
1.6071	4000	0.0084	0.0099	0.7924	-
1.7075	4250	0.008	0.0097	0.7942	-
1.8080	4500	0.0079	0.0094	0.7946	-
1.9084	4750	0.0078	0.0092	0.7928	-
2.0088	5000	0.0081	0.0088	0.7986	-
2.1093	5250	0.0056	0.0079	0.8025	-
2.2097	5500	0.0052	0.0080	0.8019	-
2.3102	5750	0.0048	0.0079	0.8073	-
2.4106	6000	0.0053	0.0081	0.8058	-
2.5110	6250	0.0049	0.0079	0.8091	-
2.6115	6500	0.0053	0.0077	0.8081	-
2.7119	6750	0.0052	0.0075	0.8075	-
2.8124	7000	0.0049	0.0077	0.8089	-
2.9128	7250	0.0051	0.0076	0.8066	-
3.0133	7500	0.0048	0.0074	0.8127	-
3.1137	7750	0.0034	0.0069	0.8162	-
3.2141	8000	0.0033	0.0070	0.8164	-
3.3146	8250	0.0036	0.0068	0.8194	-
3.4150	8500	0.0032	0.0069	0.8156	-
3.5155	8750	0.0032	0.0068	0.8196	-
3.6159	9000	0.0032	0.0067	0.8197	-
3.7164	9250	0.0034	0.0068	0.8194	-
3.8168	9500	0.0034	0.0066	0.8194	-
3.9172	9750	0.0032	0.0063	0.8239	-
4.0177	10000	0.0032	0.0065	0.8229	-
4.1181	10250	0.0023	0.0063	0.8258	-
4.2186	10500	0.0022	0.0062	0.8293	-
4.3190	10750	0.002	0.0063	0.8283	-
4.4194	11000	0.0023	0.0060	0.8306	-
4.5199	11250	0.0021	0.0061	0.8318	-
4.6203	11500	0.0021	0.0060	0.8290	-
4.7208	11750	0.0022	0.0058	0.8329	-
4.8212	12000	0.0021	0.0058	0.8342	-
4.9217	12250	0.0021	0.0058	0.8360	-
5.0221	12500	0.0017	0.0057	0.8355	-
5.1225	12750	0.0014	0.0057	0.8377	-
5.2230	13000	0.0015	0.0058	0.8363	-
5.3234	13250	0.0015	0.0058	0.8369	-
5.4239	13500	0.0014	0.0057	0.8386	-
5.5243	13750	0.0014	0.0057	0.8387	-
5.6247	14000	0.0015	0.0057	0.8392	-
5.7252	14250	0.0014	0.0056	0.8409	-
5.8256	14500	0.0014	0.0056	0.8410	-
5.9261	14750	0.0014	0.0056	0.8410	-
6.0	14934	-	-	-	0.8379

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.3.1
• Transformers: 4.48.0
• PyTorch: 2.5.1+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",
}