tes_upload / README.md

yahyaabd

Upload model from local path: models\paraphrase-multilingual-miniLM-L12-v2-finetuned-bps-all

97edd48 verified 5 months ago

preview code

raw

history blame contribute delete

18 kB

metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:350
  - loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
widget:
  - source_sentence: >-
      Data pengeluaran bulanan rumah tangga pedesaan untuk konsumsi makanan dan
      non-makanan per provinsi, tahun berapa saja tersedia?
    sentences:
      - Sistem Neraca Sosial Ekonomi Indonesia Tahun 2022 (84 x 84)
      - >-
        Persentase RataRata Pengeluaran per Kapita Sebulan Untuk Makanan dan
        Bukan Makanan di Daerah Perdesaan Menurut Provinsi, 2007-2024
      - >-
        Nilai Impor Jawa Madura Menurut Pelabuhan Impor di Pulau Jawa Madura
        Tahun 2009 - 2013 (Juta US $) 1)
  - source_sentence: Asal impor gula Indonesia periode 2017 hingga 2023
    sentences:
      - >-
        Banyaknya Anggota Kadinda Menurut Kabupaten/Kota di Provinsi Jawa
        Tengah, 2019
      - Impor Gula menurut Negara Asal Utama, 2017-2023
      - >-
        Rata-rata Pendapatan Bersih Pekerja Bebas Menurut Provinsi dan Kelompok
        Umur, 2023
  - source_sentence: Laju kehilangan hutan Indonesia dalam dan luar kawasan hutan 2013-2022.
    sentences:
      - >-
        Institusi Pemerintah Neraca Institusi Terintegrasi (Triliun Rupiah),
        2016 2023
      - >-
        Angka Deforestasi (Netto) Indonesia di Dalam dan di Luar Kawasan Hutan
        Tahun 2013-2022 (Ha/Th)
      - >-
        Produksi Perkebunan Menurut Kabupaten/Kota dan Jenis Tanaman di Provinsi
        Jawa Tengah (ton), 2021 dan 2022
  - source_sentence: Kemana saja lada putih Indonesia diekspor pada periode 2012 sampai 2023?
    sentences:
      - >-
        Rata-rata Pendapatan Bersih Pekerja Bebas Menurut Provinsi dan Kelompok
        Umur, 2022-2023
      - Ekspor Lada Putih menurut Negara Tujuan Utama, 2012-2023
      - >-
        Angka Kelahiran Kasar (Crude Birth Rate) Hasil Long Form SP2020 Menurut
        Provinsi/Kabupaten/Kota, 2020
  - source_sentence: >-
      data gaji bersih pegawai per bulan tahun 2023 berdasarkan pendidikan dan
      jenis pekerjaan utama
    sentences:
      - >-
        Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut
        Pendidikan Tertinggi yang Ditamatkan dan Jenis Pekerjaan Utama, 2023
      - >-
        Banyaknya Kunjungan Kapal Melalui Pelabuhan Jepara Menurut Jenis
        Pelayaran Tahun 2009 - 2013
      - Ekspor Sarang Burung menurut Negara Tujuan Utama, 2012-2023
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
model-index:
  - name: >-
      SentenceTransformer based on
      sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: bps val mfd all
          type: bps-val-mfd-all
        metrics:
          - type: cosine_accuracy@1
            value: 0.9861111111111112
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9861111111111112
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9861111111111112
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9861111111111112
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.9861111111111112
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.9351851851851851
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.9055555555555554
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.8333333333333334
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.016151592322246593
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.0425075387306992
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.06836160354671791
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.11202747994449548
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8706665539282586
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9861111111111112
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.44673547368787836
            name: Cosine Map@100

SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 on the csv dataset. 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: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
Maximum Sequence Length: 128 tokens
Output Dimensionality: 384 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- csv

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: 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 = [
    'data gaji bersih pegawai per bulan tahun 2023 berdasarkan pendidikan dan jenis pekerjaan utama',
    'Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Pendidikan Tertinggi yang Ditamatkan dan Jenis Pekerjaan Utama, 2023',
    'Banyaknya Kunjungan Kapal Melalui Pelabuhan Jepara Menurut Jenis Pelayaran Tahun 2009 - 2013',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

Dataset: bps-val-mfd-all
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.9861
cosine_accuracy@3	0.9861
cosine_accuracy@5	0.9861
cosine_accuracy@10	0.9861
cosine_precision@1	0.9861
cosine_precision@3	0.9352
cosine_precision@5	0.9056
cosine_precision@10	0.8333
cosine_recall@1	0.0162
cosine_recall@3	0.0425
cosine_recall@5	0.0684
cosine_recall@10	0.112
cosine_ndcg@10	0.8707
cosine_mrr@10	0.9861
cosine_map@100	0.4467

Training Details

Training Dataset

csv

Dataset: csv
Size: 350 training samples
Columns: query, positive, and negative

Approximate statistics based on the first 350 samples:

	query	positive	negative
type	string	string	string
details	min: 7 tokens mean: 16.16 tokens max: 31 tokens	min: 9 tokens mean: 23.2 tokens max: 49 tokens	min: 5 tokens mean: 27.02 tokens max: 59 tokens

Samples:

query	positive	negative
`Bagaimana pengeluaran rumah tangga per orang di Indonesia berubah dari 2010 sampai 2024?`	`Distribusi Pembagian Pengeluaran per Kapita dan Indeks Gini, 2010-2024`	`Proyeksi Beban Pencemaran Udara Menurut Industri di Jawa Tengah Tahun 2020 (Ton/Tahun)`
`Data kesenjangan pendapatan di Indonesia tahun 2010-2024: indeks Gini dan pengeluaran rata-rata.`	`Distribusi Pembagian Pengeluaran per Kapita dan Indeks Gini, 2010-2024`	`Banyaknya Mahasiswa dan Dosen Pada Perguruan Tinggi Agama Islam Swasta di Jawa Tengah, 2018/2019`
`Berapa konsumsi makanan pokok per orang per minggu di Indonesia tahun 2007-2024?`	`Rata-Rata Konsumsi per Kapita Seminggu Beberapa Macam Bahan Makanan Penting, 2007-2024`	`Rekapitulasi Industri Non Formal Yang Baru Menurut Kabupaten/kota 2012`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
weight_decay: 0.01
warmup_ratio: 0.1
fp16: True
load_best_model_at_end: 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: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 3
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: 0
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: 0.0
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
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: proportional

Training Logs

Epoch	Step	bps-val-mfd-all_cosine_ndcg@10
0.9091	10	0.8300
1.8182	20	0.8736
2.7273	30	0.8707

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.11
Sentence Transformers: 3.4.0
Transformers: 4.53.1
PyTorch: 2.7.1+cpu
Accelerate: 1.8.1
Datasets: 3.6.0
Tokenizers: 0.21.2

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",
}

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}
}