SentenceTransformer based on indobenchmark/indobert-base-p2
This is a sentence-transformers model finetuned from indobenchmark/indobert-base-p2 on the afaji/indonli 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: indobenchmark/indobert-base-p2
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 768 tokens
- Similarity Function: Cosine Similarity
- Training Dataset:
- Language: id
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': 512, 'do_lower_case': False}) with Transformer model: BertModel
(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("cassador/2bs4lr2")
# Run inference
sentences = [
'Tujuan dari acara dengar pendapat CRTC adalah untuk mengumpulkan respons dari pada pemangku kepentingan industri ini dan dari masyarakat umum.',
'Masyarakat umum dilibatkan untuk memberikan respon dalam acara dengar pendapat CRTC.',
'Pembuat Rooms hanya bisa membuat meeting yang terbuka.',
]
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
- Dataset:
sts-dev
- Evaluated with
EmbeddingSimilarityEvaluator
Metric | Value |
---|---|
pearson_cosine | 0.5893 |
spearman_cosine | 0.5802 |
pearson_manhattan | 0.5822 |
spearman_manhattan | 0.5749 |
pearson_euclidean | 0.5862 |
spearman_euclidean | 0.5765 |
pearson_dot | 0.591 |
spearman_dot | 0.5847 |
pearson_max | 0.591 |
spearman_max | 0.5847 |
Semantic Similarity
- Dataset:
sts-test
- Evaluated with
EmbeddingSimilarityEvaluator
Metric | Value |
---|---|
pearson_cosine | 0.2927 |
spearman_cosine | 0.267 |
pearson_manhattan | 0.2598 |
spearman_manhattan | 0.2546 |
pearson_euclidean | 0.2669 |
spearman_euclidean | 0.2563 |
pearson_dot | 0.3098 |
spearman_dot | 0.2953 |
pearson_max | 0.3098 |
spearman_max | 0.2953 |
Training Details
Training Dataset
afaji/indonli
- Dataset: afaji/indonli
- Size: 6,915 training samples
- Columns:
premise
,hypothesis
, andlabel
- Approximate statistics based on the first 1000 samples:
premise hypothesis label type string string int details - min: 12 tokens
- mean: 29.26 tokens
- max: 135 tokens
- min: 6 tokens
- mean: 12.13 tokens
- max: 36 tokens
- 0: ~51.00%
- 1: ~49.00%
- Samples:
premise hypothesis label Presiden Joko Widodo (Jokowi) menyampaikan prediksi bahwa wabah virus Corona (COVID-19) di Indonesia akan selesai akhir tahun ini.
Prediksi akhir wabah tidak disampaikan Jokowi.
0
Meski biasanya hanya digunakan di fasilitas kesehatan, saat ini masker dan sarung tangan sekali pakai banyak dipakai di tingkat rumah tangga.
Masker sekali pakai banyak dipakai di tingkat rumah tangga.
1
Seperti namanya, paket internet sahur Telkomsel ini ditujukan bagi pengguna yang menginginkan kuota ekstra, untuk menemani momen sahur sepanjang bulan puasa.
Paket internet sahur tidak ditujukan untuk saat sahur.
0
- Loss:
SoftmaxLoss
Evaluation Dataset
afaji/indonli
- Dataset: afaji/indonli
- Size: 1,556 evaluation samples
- Columns:
premise
,hypothesis
, andlabel
- Approximate statistics based on the first 1000 samples:
premise hypothesis label type string string int details - min: 9 tokens
- mean: 28.07 tokens
- max: 179 tokens
- min: 6 tokens
- mean: 12.15 tokens
- max: 25 tokens
- 0: ~47.90%
- 1: ~52.10%
- Samples:
premise hypothesis label Manuskrip tersebut berisi tiga catatan yang menceritakan bagaimana peristiwa jatuhnya meteorit serta laporan kematian akibat kejadian tersebut seperti dilansir dari Science Alert, Sabtu (25/4/2020).
Manuskrip tersebut tidak mencatat laporan kematian.
0
Dilansir dari Business Insider, menurut observasi dari Mauna Loa Observatory di Hawaii pada karbon dioksida (CO2) di level mencapai 410 ppm tidak langsung memberikan efek pada pernapasan, karena tubuh manusia juga masih membutuhkan CO2 dalam kadar tertentu.
Tidak ada observasi yang pernah dilansir oleh Business Insider.
0
Seorang wanita asal New York mengaku sangat benci air putih.
Tidak ada orang dari New York yang membenci air putih.
0
- Loss:
SoftmaxLoss
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: epochper_device_train_batch_size
: 4per_device_eval_batch_size
: 4learning_rate
: 2e-05num_train_epochs
: 2warmup_ratio
: 0.1fp16
: True
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: epochprediction_loss_only
: Trueper_device_train_batch_size
: 4per_device_eval_batch_size
: 4per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonelearning_rate
: 2e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 2max_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
: 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
: Falsebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine | sts-test_spearman_cosine |
---|---|---|---|---|---|
0 | 0 | - | - | 0.1277 | - |
0.0578 | 100 | 0.6725 | - | - | - |
0.1157 | 200 | 0.5884 | - | - | - |
0.1735 | 300 | 0.5397 | - | - | - |
0.2313 | 400 | 0.583 | - | - | - |
0.2892 | 500 | 0.6089 | - | - | - |
0.3470 | 600 | 0.5719 | - | - | - |
0.4049 | 700 | 0.6327 | - | - | - |
0.4627 | 800 | 0.5983 | - | - | - |
0.5205 | 900 | 0.5009 | - | - | - |
0.5784 | 1000 | 0.6115 | - | - | - |
0.6362 | 1100 | 0.5186 | - | - | - |
0.6940 | 1200 | 0.5574 | - | - | - |
0.7519 | 1300 | 0.5939 | - | - | - |
0.8097 | 1400 | 0.5022 | - | - | - |
0.8676 | 1500 | 0.5355 | - | - | - |
0.9254 | 1600 | 0.532 | - | - | - |
0.9832 | 1700 | 0.4894 | - | - | - |
1.0 | 1729 | - | 0.4545 | 0.5332 | - |
1.0411 | 1800 | 0.4036 | - | - | - |
1.0989 | 1900 | 0.4111 | - | - | - |
1.1567 | 2000 | 0.3725 | - | - | - |
1.2146 | 2100 | 0.4287 | - | - | - |
1.2724 | 2200 | 0.3846 | - | - | - |
1.3302 | 2300 | 0.387 | - | - | - |
1.3881 | 2400 | 0.361 | - | - | - |
1.4459 | 2500 | 0.4419 | - | - | - |
1.5038 | 2600 | 0.3893 | - | - | - |
1.5616 | 2700 | 0.4324 | - | - | - |
1.6194 | 2800 | 0.3965 | - | - | - |
1.6773 | 2900 | 0.4438 | - | - | - |
1.7351 | 3000 | 0.3788 | - | - | - |
1.7929 | 3100 | 0.4741 | - | - | - |
1.8508 | 3200 | 0.27 | - | - | - |
1.9086 | 3300 | 0.4175 | - | - | - |
1.9665 | 3400 | 0.5599 | - | - | - |
2.0 | 3458 | - | 0.5981 | 0.5802 | 0.2670 |
Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.0+cu121
- Accelerate: 0.31.0
- Datasets: 2.20.0
- Tokenizers: 0.19.1
Citation
BibTeX
Sentence Transformers and SoftmaxLoss
@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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Model tree for cassador/2bs4lr2
Base model
indobenchmark/indobert-base-p2Dataset used to train cassador/2bs4lr2
Evaluation results
- Pearson Cosine on sts devself-reported0.589
- Spearman Cosine on sts devself-reported0.580
- Pearson Manhattan on sts devself-reported0.582
- Spearman Manhattan on sts devself-reported0.575
- Pearson Euclidean on sts devself-reported0.586
- Spearman Euclidean on sts devself-reported0.577
- Pearson Dot on sts devself-reported0.591
- Spearman Dot on sts devself-reported0.585
- Pearson Max on sts devself-reported0.591
- Spearman Max on sts devself-reported0.585