Add new SentenceTransformer model

c1607e0 verified about 1 year ago

20.1 kB

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:64260
  - loss:CosineSimilarityLoss
base_model: yahyaabd/allstats-search-mini-v1-1-mnrl
widget:
  - source_sentence: q-2216
    sentences:
      - Statistik Potensi Desa Provinsi Jambi 2008
      - Indeks Harga Sahsm
      - 17cb76daaeda2a9d92a30af3
  - source_sentence: q-4069
    sentences:
      - 61e74412ad7c948492537b61
      - Ihpb Indonesia Tahun 2014
      - Indeks Harga Perdagangan Besar Indonesia 2014, 2010=100
  - source_sentence: q-748
    sentences:
      - 20dac9022b69b62ab3479d37
      - Statistik Potensi Desa Provinsi Sulawesi Utara 2014
      - data potensi dpsa di Provinsi Sulawesi Utara tahun 2014
  - source_sentence: q-7475
    sentences:
      - >-
        Harga Konsumen Beberapa Barang dan Jasa Kelompok Kesehatan,
        Transportasi, dan Pendidikan 90 Kota di Indonesia 2021
      - Volume ekspor CPO Indonesia
      - b2dbf308898a6d1748629240
  - source_sentence: q-786
    sentences:
      - Statistik eCommerce 2022/2023
      - Angka Kematian Bayi oper P#rovinsi
      - f3b02f2b6706e104ea9d5b74
datasets:
  - yahyaabd/bps-pub-cosine-pairs
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
model-index:
  - name: SentenceTransformer based on yahyaabd/allstats-search-mini-v1-1-mnrl
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev
          type: sts-dev
        metrics:
          - type: pearson_cosine
            value: 0.9040861364751858
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8334861589775715
            name: Spearman Cosine
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test
          type: sts-test
        metrics:
          - type: pearson_cosine
            value: 0.9069041337320248
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8380868510850786
            name: Spearman Cosine

SentenceTransformer based on yahyaabd/allstats-search-mini-v1-1-mnrl

This is a sentence-transformers model finetuned from yahyaabd/allstats-search-mini-v1-1-mnrl on the bps-pub-cosine-pairs 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: yahyaabd/allstats-search-mini-v1-1-mnrl
Maximum Sequence Length: 128 tokens
Output Dimensionality: 384 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- bps-pub-cosine-pairs

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("yahyaabd/allstats-search-mini-v2")
# Run inference
sentences = [
    'q-786',
    'Angka Kematian Bayi oper P#rovinsi',
    'f3b02f2b6706e104ea9d5b74',
]
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

Semantic Similarity

Datasets: sts-dev and sts-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	sts-dev	sts-test
pearson_cosine	0.9041	0.9069
spearman_cosine	0.8335	0.8381

Training Details

Training Dataset

bps-pub-cosine-pairs

Dataset: bps-pub-cosine-pairs at 038a9de
Size: 64,260 training samples
Columns: query_id, query, corpus_id, title, and score

Approximate statistics based on the first 1000 samples:

	query_id	query	corpus_id	title	score
type	string	string	string	string	float
details	min: 4 tokens mean: 5.18 tokens max: 6 tokens	min: 4 tokens mean: 13.33 tokens max: 38 tokens	min: 7 tokens mean: 17.38 tokens max: 22 tokens	min: 5 tokens mean: 13.13 tokens max: 30 tokens	min: 0.1 mean: 0.56 max: 0.9

Samples:

query_id	query	corpus_id	title	score
`q-1599`	`Nilai Tukar Nelayan`	`0b0da8fc2b6af9329a6d9cfe`	`Statistik Hotel dan Akomodasi Lainnya di Indonesia 2013`	`0.1`
`q-1599`	`nilai tukar nelayan`	`0b0da8fc2b6af9329a6d9cfe`	`Statistik Hotel dan Akomodasi Lainnya di Indonesia 2013`	`0.1`
`q-1599`	`NILAI TUKAR NELAYAN`	`0b0da8fc2b6af9329a6d9cfe`	`Statistik Hotel dan Akomodasi Lainnya di Indonesia 2013`	`0.1`

Loss: CosineSimilarityLoss with these parameters:

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

Evaluation Dataset

bps-pub-cosine-pairs

Dataset: bps-pub-cosine-pairs at 038a9de
Size: 8,067 evaluation samples
Columns: query_id, query, corpus_id, title, and score

Approximate statistics based on the first 1000 samples:

	query_id	query	corpus_id	title	score
type	string	string	string	string	float
details	min: 4 tokens mean: 5.2 tokens max: 6 tokens	min: 4 tokens mean: 12.77 tokens max: 33 tokens	min: 13 tokens mean: 17.25 tokens max: 23 tokens	min: 5 tokens mean: 13.37 tokens max: 38 tokens	min: 0.1 mean: 0.57 max: 0.9

Samples:

query_id	query	corpus_id	title	score
`q-1273`	`Sosek Desember 2021`	`b7890a143bc751d1d84dcf4a`	`Laporan Bulanan Data Sosial Ekonomi Desember 2021`	`0.9`
`q-1273`	`sosek desember 2021`	`b7890a143bc751d1d84dcf4a`	`Laporan Bulanan Data Sosial Ekonomi Desember 2021`	`0.9`
`q-1273`	`SOSEK DESEMBER 2021`	`b7890a143bc751d1d84dcf4a`	`Laporan Bulanan Data Sosial Ekonomi Desember 2021`	`0.9`

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: 64
per_device_eval_batch_size: 64
learning_rate: 1e-05
num_train_epochs: 2
warmup_ratio: 0.1
fp16: True
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: 64
per_device_eval_batch_size: 64
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: 1e-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: 2
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: 0.01
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	sts-dev_spearman_cosine	sts-test_spearman_cosine
0	0	-	0.3848	0.8288	-
0.0995	100	0.236	0.0950	0.8396	-
0.1990	200	0.0655	0.0487	0.8452	-
0.2985	300	0.0407	0.0342	0.8437	-
0.3980	400	0.0309	0.0291	0.8427	-
0.4975	500	0.0247	0.0253	0.8427	-
0.5970	600	0.0211	0.0235	0.8427	-
0.6965	700	0.0198	0.0224	0.8395	-
0.7960	800	0.0168	0.0212	0.8405	-
0.8955	900	0.0166	0.0206	0.8384	-
0.9950	1000	0.0145	0.0195	0.8388	-
1.0945	1100	0.0119	0.0193	0.8395	-
1.1940	1200	0.0113	0.0190	0.8376	-
1.2935	1300	0.0108	0.0189	0.8330	-
1.3930	1400	0.0119	0.0180	0.8364	-
1.4925	1500	0.0105	0.0184	0.8338	-
1.5920	1600	0.0092	0.0180	0.8355	-
1.6915	1700	0.009	0.0182	0.8319	-
1.7910	1800	0.0096	0.0178	0.8337	-
1.8905	1900	0.0099	0.0178	0.8326	-
1.99	2000	0.0094	0.0178	0.8335	-
-1	-1	-	-	-	0.8381

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.4.0
Transformers: 4.48.1
PyTorch: 2.5.1+cu124
Accelerate: 1.3.0
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",
}