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---
language:
- id
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:10000
- loss:SoftmaxLoss
base_model: indobenchmark/indobert-base-p2
datasets:
- afaji/indonli
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: '"Berbagai macam jenis minuman sehat untuk mengembalikan ion ataupun
mengandung vitamin, dapat kita temui dengan mudah di sekitar."'
sentences:
- Moody's tidak memiliki metrik peringkat untuk penerbit sekuritas yang dikenai
pajak.
- Lupa olahraga adalah alasan yang selalu digunakan untuk tak berolahraga.
- Minuman sehat sulit ditemui.
- source_sentence: Mayweather menepis anggapan bahwa McGregor yang merupakan petarung
kidal mungkin menyebabkan masalah baginya.
sentences:
- Cimahi Selatan merupakan sebuah Kecamatan di Kota Cimahi.
- Masyarakat umum dilibatkan untuk memberikan respon dalam acara dengar pendapat
CRTC.
- McGregor dan Mayweather pernah bertarung dengan sengit.
- source_sentence: Wonosobo adalah salah satu kabupaten yang terdapat di Provinsi
Jawa Tengah.
sentences:
- Tidak terdapat kabupaten di Provinsi Jawa Tengah.
- Nogizaka46 sekarang sudah merilis 25 singel.
- Joko Driyono adalah Wakil Ketua Umum PSSI.
- source_sentence: Bangunan ini digunakan untuk penjualan berbagai material. '
sentences:
- Istri bisa mengidamkan makanan yang mudah dicari.
- Saluran telepon tidak digunakan oleh FastNet dalam menyediakan akses internet.
- Bangunan ini digunakan untuk penjualan.
- source_sentence: Set album musik pengiring seri film Harry Potter akan dirilis dalam
versi baru.
sentences:
- Seri film Harry Potter memiliki set album musik pengiring.
- Daya tahan tubuh bayi tidak terjaga walaupun diberi ASI.
- Laga dan kolosal adalah genre film.
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on indobenchmark/indobert-base-p2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts-dev
metrics:
- type: pearson_cosine
value: 0.3021139089985203
name: Pearson Cosine
- type: spearman_cosine
value: 0.30301169986128346
name: Spearman Cosine
- type: pearson_manhattan
value: 0.2767840491173264
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.2725949754810958
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.3071661849384816
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.3044966278223258
name: Spearman Euclidean
- type: pearson_dot
value: 0.3039090779569512
name: Pearson Dot
- type: spearman_dot
value: 0.3047234168200123
name: Spearman Dot
- type: pearson_max
value: 0.3071661849384816
name: Pearson Max
- type: spearman_max
value: 0.3047234168200123
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.10382066164158449
name: Pearson Cosine
- type: spearman_cosine
value: 0.09693567465932618
name: Spearman Cosine
- type: pearson_manhattan
value: 0.07492996229311771
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.07823414156216839
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.09422022261567607
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.09902189422521299
name: Spearman Euclidean
- type: pearson_dot
value: 0.10695495102872325
name: Pearson Dot
- type: spearman_dot
value: 0.09978448101169902
name: Spearman Dot
- type: pearson_max
value: 0.10695495102872325
name: Pearson Max
- type: spearman_max
value: 0.09978448101169902
name: Spearman Max
---
# SentenceTransformer based on indobenchmark/indobert-base-p2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) on the [afaji/indonli](https://huggingface.co/datasets/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](https://huggingface.co/indobenchmark/indobert-base-p2) <!-- at revision 94b4e0a82081fa57f227fcc2024d1ea89b57ac1f -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [afaji/indonli](https://huggingface.co/datasets/afaji/indonli)
- **Language:** id
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
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]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.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 [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.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 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### afaji/indonli
* Dataset: [afaji/indonli](https://huggingface.co/datasets/afaji/indonli)
* Size: 10,000 training samples
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | premise | hypothesis | label |
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 12 tokens</li><li>mean: 29.73 tokens</li><li>max: 179 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.93 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>0: ~68.60%</li><li>1: ~31.40%</li></ul> |
* Samples:
| premise | hypothesis | label |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------|:---------------|
| <code>Presiden Joko Widodo (Jokowi) menyampaikan prediksi bahwa wabah virus Corona (COVID-19) di Indonesia akan selesai akhir tahun ini.</code> | <code>Prediksi akhir wabah tidak disampaikan Jokowi.</code> | <code>0</code> |
| <code>Meski biasanya hanya digunakan di fasilitas kesehatan, saat ini masker dan sarung tangan sekali pakai banyak dipakai di tingkat rumah tangga.</code> | <code>Masker sekali pakai banyak dipakai di tingkat rumah tangga.</code> | <code>1</code> |
| <code>Data dari Nielsen Music mencatat, "Joanne" telah terjual 201 ribu kopi di akhir minggu ini, seperti dilansir aceshowbiz.com.</code> | <code>Nielsen Music mencatat pada akhir minggu ini.</code> | <code>0</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
### Evaluation Dataset
#### afaji/indonli
* Dataset: [afaji/indonli](https://huggingface.co/datasets/afaji/indonli)
* Size: 2,000 evaluation samples
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | premise | hypothesis | label |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 9 tokens</li><li>mean: 28.09 tokens</li><li>max: 179 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.01 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>0: ~63.00%</li><li>1: ~37.00%</li></ul> |
* Samples:
| premise | hypothesis | label |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------|:---------------|
| <code>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).</code> | <code>Manuskrip tersebut tidak mencatat laporan kematian.</code> | <code>0</code> |
| <code>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.</code> | <code>Tidak ada observasi yang pernah dilansir oleh Business Insider.</code> | <code>0</code> |
| <code>Perekonomian Jakarta terutama ditunjang oleh sektor perdagangan, jasa, properti, industri kreatif, dan keuangan.</code> | <code>Sektor jasa memberi pengaruh lebih besar daripada industri kreatif dalam perekonomian Jakarta.</code> | <code>0</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `learning_rate`: 1e-05
- `num_train_epochs`: 10
- `warmup_ratio`: 0.001
- `fp16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_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`: 10
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.001
- `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`: 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}
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| 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 |
</details>
### 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
```bibtex
@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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