indonesian-embedding-small / pytorch /README.md

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Initial Release: Indonesian Embedding Small with PyTorch and ONNX variants...

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metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - dataset_size:10554
  - loss:CosineSimilarityLoss
base_model: LazarusNLP/all-indo-e5-small-v4
widget:
  - source_sentence: Menggunakan sunscreen setiap hari
    sentences:
      - >-
        Seorang anak laki-laki yang tampak sakit disentuh wajahnya oleh seorang
        balita.
      - >-
        Warga Hispanik secara resmi telah menyalip warga Amerika keturunan
        Afrika sebagai kelompok minoritas terbesar di AS

        menurut laporan yang dirilis oleh Biro Sensus AS.
      - Tidak pernah menggunakan sunscreen
  - source_sentence: Sering membeli makanan siap saji melalui aplikasi
    sentences:
      - Provinsi ini memiliki angka kepadatan penduduk 38 jiwa/km².
      - Kadang membeli makanan siap saji melalui aplikasi
      - Seorang pria sedang melakukan trik kartu.
  - source_sentence: >-
      University of Michigan hari ini merilis kebijakan penerimaan mahasiswa
      baru setelah Mahkamah Agung AS membatalkan cara penerimaan mahasiswa baru
      yang sebelumnya.
    sentences:
      - >-
        "Mereka telah memblokir semua tanaman bio baru karena ketakutan yang
        tidak berdasar dan tidak ilmiah," kata Bush.
      - Jarang membeli kopi Kenangan
      - >-
        University of Michigan berencana untuk merilis kebijakan penerimaan
        mahasiswa baru pada hari Kamis setelah persyaratan penerimaannya ditolak
        oleh Mahkamah Agung AS pada bulan Juni.
  - source_sentence: >-
      pakar non-proliferasi di institut internasional untuk studi strategis mark
      fitzpatrick menyatakan bahwa laporan IAEA - memiliki tenor yang sangat
      kuat.
    sentences:
      - Pernah membeli kopi Starbucks
      - >-
        rekan senior di institut internasional untuk studi strategis mark
        fitzpatrick menyatakan bahwa - rencana badan energi atom internasional
        adalah dangkal.
      - Korea Utara mengusulkan pembicaraan tingkat tinggi dengan AS
  - source_sentence: Palestina dan Yordania koordinasikan sikap dalam perundingan damai
    sentences:
      - Petinggi Hamas bantah Gaza dan PA berkoordinasi dalam perundingan damai
      - Tidak pernah memesan makanan lewat aplikasi
      - Kereta api yang melaju di atas rel.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
model-index:
  - name: SentenceTransformer based on LazarusNLP/all-indo-e5-small-v4
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts indo detailed
          type: sts-indo-detailed
        metrics:
          - type: pearson_cosine
            value: 0.8612625897174441
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8586969176298713
            name: Spearman Cosine

SentenceTransformer based on LazarusNLP/all-indo-e5-small-v4

This is a sentence-transformers model finetuned from LazarusNLP/all-indo-e5-small-v4. It maps sentences & paragraphs to a 384-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: LazarusNLP/all-indo-e5-small-v4
Maximum Sequence Length: 384 tokens
Output Dimensionality: 384 dimensions
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': 384, 'do_lower_case': False, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Palestina dan Yordania koordinasikan sikap dalam perundingan damai',
    'Petinggi Hamas bantah Gaza dan PA berkoordinasi dalam perundingan damai',
    'Kereta api yang melaju di atas rel.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.5014, -0.0652],
#         [ 0.5014,  1.0000, -0.0518],
#         [-0.0652, -0.0518,  1.0000]])

Evaluation

Metrics

Semantic Similarity

Dataset: sts-indo-detailed
Evaluated with main.DetailedEmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8613
spearman_cosine	0.8587

Training Details

Training Dataset

Unnamed Dataset

Size: 10,554 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label
type string string float
details
min: 5 tokens
mean: 14.45 tokens
max: 50 tokens

min: 5 tokens
mean: 14.19 tokens
max: 50 tokens

min: 0.0
mean: 0.47
max: 1.0

	sentence_0	sentence_1	label
type	string	string	float
details	min: 5 tokens mean: 14.45 tokens max: 50 tokens	min: 5 tokens mean: 14.19 tokens max: 50 tokens	min: 0.0 mean: 0.47 max: 1.0

Samples:

sentence_0	sentence_1	label
`Tidak pernah mengisi saldo ShopeePay`	`Tidak pernah mengisi saldo GoPay`	`0.0`
`PM Turki mendesak untuk mengakhiri protes di Istanbul`	`Polisi Turki menembakkan gas air mata ke arah pengunjuk rasa di Istanbul`	`0.56`
`Dua ekor kucing sedang melihat ke arah jendela.`	`Seekor kucing putih yang sedang melihat ke luar jendela.`	`0.5199999809265137`

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: 6
per_device_eval_batch_size: 6
num_train_epochs: 7
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 6
per_device_eval_batch_size: 6
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
num_train_epochs: 7
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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: False
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: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
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
hub_revision: None
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
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: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	sts-indo-detailed_spearman_cosine
0.0569	100	-	0.8225
0.1137	200	-	0.8261
0.1706	300	-	0.8263
0.2274	400	-	0.8259
0.2843	500	0.0764	0.8273
0.3411	600	-	0.8305
0.3980	700	-	0.8319
0.4548	800	-	0.8341
0.5117	900	-	0.8345
0.5685	1000	0.0445	0.8362
0.6254	1100	-	0.8384
0.6822	1200	-	0.8391
0.7391	1300	-	0.8464
0.7959	1400	-	0.8475
0.8528	1500	0.0372	0.8471
0.9096	1600	-	0.8477
0.9665	1700	-	0.8458
1.0	1759	-	0.8464
1.0233	1800	-	0.8443
1.0802	1900	-	0.8455
1.1370	2000	0.0316	0.8481
1.1939	2100	-	0.8447
1.2507	2200	-	0.8473
1.3076	2300	-	0.8474
1.3644	2400	-	0.8449
1.4213	2500	0.0281	0.8515
1.4781	2600	-	0.8498
1.5350	2700	-	0.8506
1.5918	2800	-	0.8546
1.6487	2900	-	0.8534
1.7055	3000	0.0271	0.8512
1.7624	3100	-	0.8493
1.8192	3200	-	0.8499
1.8761	3300	-	0.8523
1.9329	3400	-	0.8518
1.9898	3500	0.0258	0.8529
2.0	3518	-	0.8535
2.0466	3600	-	0.8546
2.1035	3700	-	0.8526
2.1603	3800	-	0.8548
2.2172	3900	-	0.8504
2.2740	4000	0.0222	0.8535
2.3309	4100	-	0.8533
2.3877	4200	-	0.8538
2.4446	4300	-	0.8518
2.5014	4400	-	0.8515
2.5583	4500	0.021	0.8515
2.6151	4600	-	0.8529
2.6720	4700	-	0.8548
2.7288	4800	-	0.8552
2.7857	4900	-	0.8542
2.8425	5000	0.0209	0.8571
2.8994	5100	-	0.8552
2.9562	5200	-	0.8553
3.0	5277	-	0.8552
3.0131	5300	-	0.8560
3.0699	5400	-	0.8531
3.1268	5500	0.0199	0.8491
3.1836	5600	-	0.8515
3.2405	5700	-	0.8520
3.2973	5800	-	0.8547
3.3542	5900	-	0.8558
3.4110	6000	0.0182	0.8560
3.4679	6100	-	0.8561
3.5247	6200	-	0.8562
3.5816	6300	-	0.8547
3.6384	6400	-	0.8547
3.6953	6500	0.0171	0.8561
3.7521	6600	-	0.8563
3.8090	6700	-	0.8555
3.8658	6800	-	0.8562
3.9227	6900	-	0.8587

Framework Versions

Python: 3.11.13
Sentence Transformers: 5.1.0
Transformers: 4.56.0
PyTorch: 2.8.0
Accelerate: 1.10.1
Datasets: 4.0.0
Tokenizers: 0.22.0

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",
}