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/indobert-base-p2-nli-v2")
# Run inference
sentences = [
'Set album musik pengiring seri film Harry Potter akan dirilis dalam versi baru.',
'Seri film Harry Potter memiliki set album musik pengiring.',
'Laga dan kolosal adalah genre film.',
]
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 | 0.3021 |
spearman_cosine | 0.303 |
pearson_manhattan | 0.2768 |
spearman_manhattan | 0.2726 |
pearson_euclidean | 0.3072 |
spearman_euclidean | 0.3045 |
pearson_dot | 0.3039 |
spearman_dot | 0.3047 |
pearson_max | 0.3072 |
spearman_max | 0.3047 |
Semantic Similarity
- Dataset:
sts-test
- Evaluated with
EmbeddingSimilarityEvaluator
Metric | Value |
---|---|
pearson_cosine | 0.1038 |
spearman_cosine | 0.0969 |
pearson_manhattan | 0.0749 |
spearman_manhattan | 0.0782 |
pearson_euclidean | 0.0942 |
spearman_euclidean | 0.099 |
pearson_dot | 0.107 |
spearman_dot | 0.0998 |
pearson_max | 0.107 |
spearman_max | 0.0998 |
Training Details
Training Dataset
afaji/indonli
- Dataset: afaji/indonli
- Size: 10,000 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: 29.73 tokens
- max: 179 tokens
- min: 6 tokens
- mean: 11.93 tokens
- max: 35 tokens
- 0: ~68.60%
- 1: ~31.40%
- 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
Data dari Nielsen Music mencatat, "Joanne" telah terjual 201 ribu kopi di akhir minggu ini, seperti dilansir aceshowbiz.com.
Nielsen Music mencatat pada akhir minggu ini.
0
- Loss:
SoftmaxLoss
Evaluation Dataset
afaji/indonli
- Dataset: afaji/indonli
- Size: 2,000 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: 28.09 tokens
- max: 179 tokens
- min: 6 tokens
- mean: 12.01 tokens
- max: 24 tokens
- 0: ~63.00%
- 1: ~37.00%
- 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
Perekonomian Jakarta terutama ditunjang oleh sektor perdagangan, jasa, properti, industri kreatif, dan keuangan.
Sektor jasa memberi pengaruh lebih besar daripada industri kreatif dalam perekonomian Jakarta.
0
- Loss:
SoftmaxLoss
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: epochlearning_rate
: 1e-05num_train_epochs
: 10warmup_ratio
: 0.001fp16
: True
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: epochprediction_loss_only
: Trueper_device_train_batch_size
: 8per_device_eval_batch_size
: 8per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonelearning_rate
: 1e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 10max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.001warmup_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
Click to expand
Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine | sts-test_spearman_cosine |
---|---|---|---|---|---|
0 | 0 | - | - | 0.1928 | - |
0.04 | 100 | 1.1407 | - | - | - |
0.08 | 200 | 0.7456 | - | - | - |
0.12 | 300 | 0.6991 | - | - | - |
0.16 | 400 | 0.6653 | - | - | - |
0.2 | 500 | 0.6317 | - | - | - |
0.24 | 600 | 0.5975 | - | - | - |
0.28 | 700 | 0.5955 | - | - | - |
0.32 | 800 | 0.6168 | - | - | - |
0.36 | 900 | 0.5851 | - | - | - |
0.4 | 1000 | 0.591 | - | - | - |
0.44 | 1100 | 0.6063 | - | - | - |
0.48 | 1200 | 0.6122 | - | - | - |
0.52 | 1300 | 0.5881 | - | - | - |
0.56 | 1400 | 0.59 | - | - | - |
0.6 | 1500 | 0.5715 | - | - | - |
0.64 | 1600 | 0.5725 | - | - | - |
0.68 | 1700 | 0.5771 | - | - | - |
0.72 | 1800 | 0.5935 | - | - | - |
0.76 | 1900 | 0.584 | - | - | - |
0.8 | 2000 | 0.5829 | - | - | - |
0.84 | 2100 | 0.5507 | - | - | - |
0.88 | 2200 | 0.5447 | - | - | - |
0.92 | 2300 | 0.6059 | - | - | - |
0.96 | 2400 | 0.5389 | - | - | - |
1.0 | 2500 | 0.639 | 0.5432 | 0.4007 | - |
1.04 | 2600 | 0.463 | - | - | - |
1.08 | 2700 | 0.4936 | - | - | - |
1.12 | 2800 | 0.4966 | - | - | - |
1.16 | 2900 | 0.4588 | - | - | - |
1.2 | 3000 | 0.5148 | - | - | - |
1.24 | 3100 | 0.5043 | - | - | - |
1.28 | 3200 | 0.5048 | - | - | - |
1.32 | 3300 | 0.4803 | - | - | - |
1.3600 | 3400 | 0.465 | - | - | - |
1.4 | 3500 | 0.5133 | - | - | - |
1.44 | 3600 | 0.5505 | - | - | - |
1.48 | 3700 | 0.4498 | - | - | - |
1.52 | 3800 | 0.5418 | - | - | - |
1.56 | 3900 | 0.5268 | - | - | - |
1.6 | 4000 | 0.4546 | - | - | - |
1.6400 | 4100 | 0.5279 | - | - | - |
1.6800 | 4200 | 0.5309 | - | - | - |
1.72 | 4300 | 0.487 | - | - | - |
1.76 | 4400 | 0.5371 | - | - | - |
1.8 | 4500 | 0.5097 | - | - | - |
1.8400 | 4600 | 0.5242 | - | - | - |
1.88 | 4700 | 0.4583 | - | - | - |
1.92 | 4800 | 0.4923 | - | - | - |
1.96 | 4900 | 0.5028 | - | - | - |
2.0 | 5000 | 0.5139 | 0.6274 | 0.4335 | - |
2.04 | 5100 | 0.322 | - | - | - |
2.08 | 5200 | 0.389 | - | - | - |
2.12 | 5300 | 0.3633 | - | - | - |
2.16 | 5400 | 0.3868 | - | - | - |
2.2 | 5500 | 0.3798 | - | - | - |
2.24 | 5600 | 0.4385 | - | - | - |
2.2800 | 5700 | 0.3965 | - | - | - |
2.32 | 5800 | 0.3895 | - | - | - |
2.36 | 5900 | 0.4484 | - | - | - |
2.4 | 6000 | 0.3452 | - | - | - |
2.44 | 6100 | 0.3905 | - | - | - |
2.48 | 6200 | 0.376 | - | - | - |
2.52 | 6300 | 0.4986 | - | - | - |
2.56 | 6400 | 0.3732 | - | - | - |
2.6 | 6500 | 0.3632 | - | - | - |
2.64 | 6600 | 0.3915 | - | - | - |
2.68 | 6700 | 0.4394 | - | - | - |
2.7200 | 6800 | 0.3852 | - | - | - |
2.76 | 6900 | 0.3984 | - | - | - |
2.8 | 7000 | 0.426 | - | - | - |
2.84 | 7100 | 0.3274 | - | - | - |
2.88 | 7200 | 0.4673 | - | - | - |
2.92 | 7300 | 0.4599 | - | - | - |
2.96 | 7400 | 0.4304 | - | - | - |
3.0 | 7500 | 0.4151 | 0.8967 | 0.4007 | - |
3.04 | 7600 | 0.2345 | - | - | - |
3.08 | 7700 | 0.1807 | - | - | - |
3.12 | 7800 | 0.2984 | - | - | - |
3.16 | 7900 | 0.2357 | - | - | - |
3.2 | 8000 | 0.4506 | - | - | - |
3.24 | 8100 | 0.2178 | - | - | - |
3.2800 | 8200 | 0.2654 | - | - | - |
3.32 | 8300 | 0.2863 | - | - | - |
3.36 | 8400 | 0.2626 | - | - | - |
3.4 | 8500 | 0.3281 | - | - | - |
3.44 | 8600 | 0.2555 | - | - | - |
3.48 | 8700 | 0.4245 | - | - | - |
3.52 | 8800 | 0.2368 | - | - | - |
3.56 | 8900 | 0.3288 | - | - | - |
3.6 | 9000 | 0.3417 | - | - | - |
3.64 | 9100 | 0.3249 | - | - | - |
3.68 | 9200 | 0.3378 | - | - | - |
3.7200 | 9300 | 0.233 | - | - | - |
3.76 | 9400 | 0.3215 | - | - | - |
3.8 | 9500 | 0.251 | - | - | - |
3.84 | 9600 | 0.3138 | - | - | - |
3.88 | 9700 | 0.3081 | - | - | - |
3.92 | 9800 | 0.3875 | - | - | - |
3.96 | 9900 | 0.3231 | - | - | - |
4.0 | 10000 | 0.2119 | 1.4983 | 0.4129 | - |
4.04 | 10100 | 0.1323 | - | - | - |
4.08 | 10200 | 0.2222 | - | - | - |
4.12 | 10300 | 0.2005 | - | - | - |
4.16 | 10400 | 0.127 | - | - | - |
4.2 | 10500 | 0.1052 | - | - | - |
4.24 | 10600 | 0.1657 | - | - | - |
4.28 | 10700 | 0.2305 | - | - | - |
4.32 | 10800 | 0.1048 | - | - | - |
4.36 | 10900 | 0.2081 | - | - | - |
4.4 | 11000 | 0.201 | - | - | - |
4.44 | 11100 | 0.1515 | - | - | - |
4.48 | 11200 | 0.2112 | - | - | - |
4.52 | 11300 | 0.1936 | - | - | - |
4.5600 | 11400 | 0.1578 | - | - | - |
4.6 | 11500 | 0.2551 | - | - | - |
4.64 | 11600 | 0.2888 | - | - | - |
4.68 | 11700 | 0.128 | - | - | - |
4.72 | 11800 | 0.2172 | - | - | - |
4.76 | 11900 | 0.114 | - | - | - |
4.8 | 12000 | 0.2135 | - | - | - |
4.84 | 12100 | 0.2421 | - | - | - |
4.88 | 12200 | 0.2392 | - | - | - |
4.92 | 12300 | 0.1478 | - | - | - |
4.96 | 12400 | 0.1901 | - | - | - |
5.0 | 12500 | 0.2219 | 1.9582 | 0.3469 | - |
5.04 | 12600 | 0.1586 | - | - | - |
5.08 | 12700 | 0.1587 | - | - | - |
5.12 | 12800 | 0.0663 | - | - | - |
5.16 | 12900 | 0.0703 | - | - | - |
5.2 | 13000 | 0.0783 | - | - | - |
5.24 | 13100 | 0.1143 | - | - | - |
5.28 | 13200 | 0.1155 | - | - | - |
5.32 | 13300 | 0.0661 | - | - | - |
5.36 | 13400 | 0.0935 | - | - | - |
5.4 | 13500 | 0.1344 | - | - | - |
5.44 | 13600 | 0.1031 | - | - | - |
5.48 | 13700 | 0.1294 | - | - | - |
5.52 | 13800 | 0.103 | - | - | - |
5.5600 | 13900 | 0.0739 | - | - | - |
5.6 | 14000 | 0.1477 | - | - | - |
5.64 | 14100 | 0.1171 | - | - | - |
5.68 | 14200 | 0.1504 | - | - | - |
5.72 | 14300 | 0.1122 | - | - | - |
5.76 | 14400 | 0.1279 | - | - | - |
5.8 | 14500 | 0.0813 | - | - | - |
5.84 | 14600 | 0.1372 | - | - | - |
5.88 | 14700 | 0.1615 | - | - | - |
5.92 | 14800 | 0.1944 | - | - | - |
5.96 | 14900 | 0.0436 | - | - | - |
6.0 | 15000 | 0.1195 | 2.2220 | 0.3559 | - |
0.08 | 100 | 0.0844 | - | - | - |
0.16 | 200 | 0.1357 | - | - | - |
0.24 | 300 | 0.1382 | - | - | - |
0.32 | 400 | 0.2091 | - | - | - |
0.4 | 500 | 0.2351 | - | - | - |
0.48 | 600 | 0.2976 | - | - | - |
0.56 | 700 | 0.3408 | - | - | - |
0.64 | 800 | 0.2656 | - | - | - |
0.72 | 900 | 0.3183 | - | - | - |
0.8 | 1000 | 0.2513 | - | - | - |
0.88 | 1100 | 0.2293 | - | - | - |
0.96 | 1200 | 0.3241 | - | - | - |
1.0 | 1250 | - | 1.1813 | 0.3495 | - |
0.3195 | 100 | 0.6132 | - | - | - |
0.6390 | 200 | 0.1554 | - | - | - |
0.9585 | 300 | 0.1366 | - | - | - |
1.0 | 313 | - | 1.2867 | 0.3839 | - |
0.08 | 100 | 0.2713 | - | - | - |
0.16 | 200 | 0.1273 | - | - | - |
0.24 | 300 | 0.0883 | - | - | - |
0.32 | 400 | 0.0749 | - | - | - |
0.08 | 100 | 0.0653 | - | - | - |
0.16 | 200 | 0.0311 | - | - | - |
0.24 | 300 | 0.0368 | - | - | - |
0.32 | 400 | 0.0259 | - | - | - |
0.4 | 500 | 0.059 | - | - | - |
0.48 | 600 | 0.046 | - | - | - |
0.56 | 700 | 0.1266 | - | - | - |
0.64 | 800 | 0.0661 | - | - | - |
0.72 | 900 | 0.0676 | - | - | - |
0.8 | 1000 | 0.0759 | - | - | - |
0.88 | 1100 | 0.0527 | - | - | - |
0.96 | 1200 | 0.1038 | - | - | - |
1.0 | 1250 | - | 2.2411 | 0.3892 | - |
1.04 | 1300 | 0.0456 | - | - | - |
1.12 | 1400 | 0.1363 | - | - | - |
1.2 | 1500 | 0.1398 | - | - | - |
1.28 | 1600 | 0.1237 | - | - | - |
1.3600 | 1700 | 0.123 | - | - | - |
1.44 | 1800 | 0.1893 | - | - | - |
1.52 | 1900 | 0.1192 | - | - | - |
1.6 | 2000 | 0.1347 | - | - | - |
1.6800 | 2100 | 0.0937 | - | - | - |
1.76 | 2200 | 0.1506 | - | - | - |
1.8400 | 2300 | 0.1366 | - | - | - |
1.92 | 2400 | 0.1194 | - | - | - |
2.0 | 2500 | 0.1485 | 2.1340 | 0.3245 | - |
2.08 | 2600 | 0.0485 | - | - | - |
2.16 | 2700 | 0.0579 | - | - | - |
2.24 | 2800 | 0.0932 | - | - | - |
2.32 | 2900 | 0.0743 | - | - | - |
2.4 | 3000 | 0.0783 | - | - | - |
2.48 | 3100 | 0.0918 | - | - | - |
2.56 | 3200 | 0.0973 | - | - | - |
2.64 | 3300 | 0.0623 | - | - | - |
2.7200 | 3400 | 0.1284 | - | - | - |
2.8 | 3500 | 0.1247 | - | - | - |
2.88 | 3600 | 0.0648 | - | - | - |
2.96 | 3700 | 0.0921 | - | - | - |
3.0 | 3750 | - | 2.4354 | 0.2824 | - |
3.04 | 3800 | 0.04 | - | - | - |
3.12 | 3900 | 0.0417 | - | - | - |
3.2 | 4000 | 0.0414 | - | - | - |
3.2800 | 4100 | 0.0485 | - | - | - |
3.36 | 4200 | 0.0255 | - | - | - |
3.44 | 4300 | 0.0688 | - | - | - |
3.52 | 4400 | 0.0574 | - | - | - |
3.6 | 4500 | 0.0766 | - | - | - |
3.68 | 4600 | 0.0481 | - | - | - |
3.76 | 4700 | 0.06 | - | - | - |
3.84 | 4800 | 0.0528 | - | - | - |
3.92 | 4900 | 0.0426 | - | - | - |
4.0 | 5000 | 0.092 | 2.5427 | 0.3284 | - |
4.08 | 5100 | 0.0349 | - | - | - |
4.16 | 5200 | 0.0107 | - | - | - |
4.24 | 5300 | 0.0608 | - | - | - |
4.32 | 5400 | 0.0473 | - | - | - |
4.4 | 5500 | 0.0452 | - | - | - |
4.48 | 5600 | 0.0316 | - | - | - |
4.5600 | 5700 | 0.0096 | - | - | - |
4.64 | 5800 | 0.0511 | - | - | - |
4.72 | 5900 | 0.0207 | - | - | - |
4.8 | 6000 | 0.0061 | - | - | - |
4.88 | 6100 | 0.0381 | - | - | - |
4.96 | 6200 | 0.0378 | - | - | - |
5.0 | 6250 | - | 2.6061 | 0.3061 | - |
5.04 | 6300 | 0.0326 | - | - | - |
5.12 | 6400 | 0.0349 | - | - | - |
5.2 | 6500 | 0.0128 | - | - | - |
5.28 | 6600 | 0.0185 | - | - | - |
5.36 | 6700 | 0.0145 | - | - | - |
5.44 | 6800 | 0.0521 | - | - | - |
5.52 | 6900 | 0.0427 | - | - | - |
5.6 | 7000 | 0.0215 | - | - | - |
5.68 | 7100 | 0.0195 | - | - | - |
5.76 | 7200 | 0.0426 | - | - | - |
5.84 | 7300 | 0.057 | - | - | - |
5.92 | 7400 | 0.0106 | - | - | - |
6.0 | 7500 | 0.0284 | 2.8348 | 0.3291 | - |
6.08 | 7600 | 0.0286 | - | - | - |
6.16 | 7700 | 0.018 | - | - | - |
6.24 | 7800 | 0.0224 | - | - | - |
6.32 | 7900 | 0.0102 | - | - | - |
6.4 | 8000 | 0.0287 | - | - | - |
6.48 | 8100 | 0.0078 | - | - | - |
6.5600 | 8200 | 0.0237 | - | - | - |
6.64 | 8300 | 0.0148 | - | - | - |
6.72 | 8400 | 0.0271 | - | - | - |
6.8 | 8500 | 0.015 | - | - | - |
6.88 | 8600 | 0.0278 | - | - | - |
6.96 | 8700 | 0.0237 | - | - | - |
7.0 | 8750 | - | 2.8785 | 0.3188 | - |
7.04 | 8800 | 0.0203 | - | - | - |
7.12 | 8900 | 0.0089 | - | - | - |
7.2 | 9000 | 0.0121 | - | - | - |
7.28 | 9100 | 0.0185 | - | - | - |
7.36 | 9200 | 0.0127 | - | - | - |
7.44 | 9300 | 0.017 | - | - | - |
7.52 | 9400 | 0.0117 | - | - | - |
7.6 | 9500 | 0.006 | - | - | - |
7.68 | 9600 | 0.0061 | - | - | - |
7.76 | 9700 | 0.0141 | - | - | - |
7.84 | 9800 | 0.0091 | - | - | - |
7.92 | 9900 | 0.0164 | - | - | - |
8.0 | 10000 | 0.0244 | 2.8054 | 0.3040 | - |
8.08 | 10100 | 0.0001 | - | - | - |
8.16 | 10200 | 0.0187 | - | - | - |
8.24 | 10300 | 0.0098 | - | - | - |
8.32 | 10400 | 0.0114 | - | - | - |
8.4 | 10500 | 0.004 | - | - | - |
8.48 | 10600 | 0.0017 | - | - | - |
8.56 | 10700 | 0.0018 | - | - | - |
8.64 | 10800 | 0.009 | - | - | - |
8.72 | 10900 | 0.0047 | - | - | - |
8.8 | 11000 | 0.0014 | - | - | - |
8.88 | 11100 | 0.0049 | - | - | - |
8.96 | 11200 | 0.006 | - | - | - |
9.0 | 11250 | - | 2.9460 | 0.2967 | - |
9.04 | 11300 | 0.0057 | - | - | - |
9.12 | 11400 | 0.0051 | - | - | - |
9.2 | 11500 | 0.0067 | - | - | - |
9.28 | 11600 | 0.0009 | - | - | - |
9.36 | 11700 | 0.0046 | - | - | - |
9.44 | 11800 | 0.0138 | - | - | - |
9.52 | 11900 | 0.0067 | - | - | - |
9.6 | 12000 | 0.0043 | - | - | - |
9.68 | 12100 | 0.001 | - | - | - |
9.76 | 12200 | 0.0004 | - | - | - |
9.84 | 12300 | 0.0044 | - | - | - |
9.92 | 12400 | 0.003 | - | - | - |
10.0 | 12500 | 0.0055 | 2.9714 | 0.3030 | 0.0969 |
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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Base model
indobenchmark/indobert-base-p2Dataset used to train cassador/indobert-base-p2-nli-v2
Evaluation results
- Pearson Cosine on sts devself-reported0.302
- Spearman Cosine on sts devself-reported0.303
- Pearson Manhattan on sts devself-reported0.277
- Spearman Manhattan on sts devself-reported0.273
- Pearson Euclidean on sts devself-reported0.307
- Spearman Euclidean on sts devself-reported0.304
- Pearson Dot on sts devself-reported0.304
- Spearman Dot on sts devself-reported0.305
- Pearson Max on sts devself-reported0.307
- Spearman Max on sts devself-reported0.305