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last-sts

Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
feature-extraction
Generated from Trainer
dataset_size:353831
loss:CosineSimilarityLoss
Eval Results
Inference Endpoints
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---
language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:353831
- loss:CosineSimilarityLoss
widget:
- source_sentence: A chef is preparing some food.
sentences:
- Five birds stand on the snow.
- A chef prepared a meal.
- There is no 'still' that is not relative to some other object.
- source_sentence: A woman is adding oil on fishes.
sentences:
- Large cruise ship floating on the water.
- It refers to the maximum f-stop (which is defined as the ratio of focal length
to effective aperture diameter).
- The woman is cutting potatoes.
- source_sentence: The player shoots the winning points.
sentences:
- Minimum wage laws hurt the least skilled, least productive the most.
- The basketball player is about to score points for his team.
- Three televisions, on on the floor, the other two on a box.
- source_sentence: Stars form in star-formation regions, which itself develop from
molecular clouds.
sentences:
- Although I believe Searle is mistaken, I don't think you have found the problem.
- It may be possible for a solar system like ours to exist outside of a galaxy.
- A blond-haired child performing on the trumpet in front of a house while his younger
brother watches.
- source_sentence: While Queen may refer to both Queen regent (sovereign) or Queen
consort, the King has always been the sovereign.
sentences:
- At first, I thought this is a bit of a tricky question.
- A man plays the guitar.
- There is a very good reason not to refer to the Queen's spouse as "King" - because
they aren't the King.
datasets:
- sentence-transformers/stsb
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts-dev
metrics:
- type: pearson_cosine
value: 0.7982244251277283
name: Pearson Cosine
- type: spearman_cosine
value: 0.8130492542348773
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.7554305375132837
name: Pearson Cosine
- type: spearman_cosine
value: 0.7644057551801444
name: Spearman Cosine
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained. 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:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
<!-- - **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': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel
(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("cahya/last-sts")
# Run inference
sentences = [
'While Queen may refer to both Queen regent (sovereign) or Queen consort, the King has always been the sovereign.',
'There is a very good reason not to refer to the Queen\'s spouse as "King" - because they aren\'t the King.',
'A man plays the guitar.',
]
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
* Datasets: `sts-dev` and `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | sts-dev | sts-test |
|:--------------------|:----------|:-----------|
| pearson_cosine | 0.7982 | 0.7554 |
| **spearman_cosine** | **0.813** | **0.7644** |
<!--
## 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
#### Unnamed Dataset
* Size: 353,831 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 38.53 tokens</li><li>max: 151 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 38.78 tokens</li><li>max: 145 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.91</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------|
| <code>A long-term researcher into diabetes, he achieved significant notability with his 1988 Banting Lecture (organized annually by the American Diabetes Association in memory of Frederick Banting).</code> | <code>A renowned expert on diabetes, he gained widespread acclaim for his 1988 Banting Lecture, which is presented annually by the American Diabetes Association to commemorate Frederick Banting.</code> | <code>0.926345705986023</code> |
| <code>investigators claim the british company was a cia cover. </code> | <code>russian investigators stated that the british company was a cia cover.</code> | <code>0.88</code> |
| <code>Albert Weber (21 November 1888, in Berlin – 17 September 1940) was a German amateur football (soccer) player who competed in the 1912 Summer Olympics.</code> | <code>Albert Weber (21 November 1888, in Berlin – 17 September 1940) was a German amateur footballer who participated in the 1912 Summer Olympics.</code> | <code>0.904914379119873</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Evaluation Dataset
#### stsb
* Dataset: [stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 15.44 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.43 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:--------------------------------------------------|:------------------------------------------------------|:------------------|
| <code>A man with a hard hat is dancing.</code> | <code>A man wearing a hard hat is dancing.</code> | <code>1.0</code> |
| <code>A young child is riding a horse.</code> | <code>A child is riding a horse.</code> | <code>0.95</code> |
| <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</code> | <code>1.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"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
- `num_train_epochs`: 10
- `warmup_ratio`: 0.1
- `bf16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `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`: 5e-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`: 0.1
- `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`: True
- `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`: True
- `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`: 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`: False
- `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
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss | sts-dev_spearman_cosine | sts-test_spearman_cosine |
|:------:|:-----:|:-------------:|:---------------:|:-----------------------:|:------------------------:|
| 0.0362 | 100 | 0.0019 | 0.1114 | 0.8115 | - |
| 0.0724 | 200 | 0.0021 | 0.0882 | 0.8177 | - |
| 0.1085 | 300 | 0.0015 | 0.0748 | 0.8125 | - |
| 0.1447 | 400 | 0.0012 | 0.0679 | 0.8086 | - |
| 0.1809 | 500 | 0.0012 | 0.0608 | 0.8069 | - |
| 0.2171 | 600 | 0.001 | 0.0596 | 0.7986 | - |
| 0.2533 | 700 | 0.0011 | 0.0547 | 0.7946 | - |
| 0.2894 | 800 | 0.0011 | 0.0492 | 0.7870 | - |
| 0.3256 | 900 | 0.0009 | 0.0522 | 0.7862 | - |
| 0.3618 | 1000 | 0.0008 | 0.0519 | 0.7880 | - |
| 0.3980 | 1100 | 0.0009 | 0.0529 | 0.7962 | - |
| 0.4342 | 1200 | 0.0008 | 0.0469 | 0.7954 | - |
| 0.4703 | 1300 | 0.0009 | 0.0506 | 0.7928 | - |
| 0.5065 | 1400 | 0.0009 | 0.0466 | 0.7873 | - |
| 0.5427 | 1500 | 0.001 | 0.0495 | 0.7999 | - |
| 0.5789 | 1600 | 0.0008 | 0.0506 | 0.7861 | - |
| 0.6151 | 1700 | 0.0008 | 0.0522 | 0.7873 | - |
| 0.6512 | 1800 | 0.0009 | 0.0582 | 0.7843 | - |
| 0.6874 | 1900 | 0.0009 | 0.0585 | 0.7888 | - |
| 0.7236 | 2000 | 0.001 | 0.0508 | 0.8040 | - |
| 0.7598 | 2100 | 0.001 | 0.0483 | 0.8018 | - |
| 0.7959 | 2200 | 0.0008 | 0.0520 | 0.7841 | - |
| 0.8321 | 2300 | 0.0009 | 0.0519 | 0.7896 | - |
| 0.8683 | 2400 | 0.001 | 0.0514 | 0.7906 | - |
| 0.9045 | 2500 | 0.0009 | 0.0521 | 0.7946 | - |
| 0.9407 | 2600 | 0.0009 | 0.0496 | 0.7920 | - |
| 0.9768 | 2700 | 0.001 | 0.0566 | 0.7956 | - |
| 1.0130 | 2800 | 0.0009 | 0.0511 | 0.8044 | - |
| 1.0492 | 2900 | 0.0009 | 0.0622 | 0.8197 | - |
| 1.0854 | 3000 | 0.001 | 0.0504 | 0.8113 | - |
| 1.1216 | 3100 | 0.001 | 0.0550 | 0.8005 | - |
| 1.1577 | 3200 | 0.001 | 0.0549 | 0.7821 | - |
| 1.1939 | 3300 | 0.0009 | 0.0578 | 0.7758 | - |
| 1.2301 | 3400 | 0.0009 | 0.0543 | 0.7860 | - |
| 1.2663 | 3500 | 0.0008 | 0.0575 | 0.7891 | - |
| 1.3025 | 3600 | 0.0009 | 0.0567 | 0.7995 | - |
| 1.3386 | 3700 | 0.001 | 0.0488 | 0.7985 | - |
| 1.3748 | 3800 | 0.0009 | 0.0514 | 0.7789 | - |
| 1.4110 | 3900 | 0.001 | 0.0584 | 0.7765 | - |
| 1.4472 | 4000 | 0.001 | 0.0554 | 0.7888 | - |
| 1.4834 | 4100 | 0.001 | 0.0659 | 0.7959 | - |
| 1.5195 | 4200 | 0.0009 | 0.0511 | 0.7816 | - |
| 1.5557 | 4300 | 0.0009 | 0.0555 | 0.7826 | - |
| 1.5919 | 4400 | 0.001 | 0.0525 | 0.7944 | - |
| 1.6281 | 4500 | 0.0009 | 0.0553 | 0.7941 | - |
| 1.6643 | 4600 | 0.001 | 0.0588 | 0.7984 | - |
| 1.7004 | 4700 | 0.001 | 0.0579 | 0.8004 | - |
| 1.7366 | 4800 | 0.0009 | 0.0540 | 0.7916 | - |
| 1.7728 | 4900 | 0.0009 | 0.0557 | 0.7963 | - |
| 1.8090 | 5000 | 0.0008 | 0.0536 | 0.8044 | - |
| 1.8452 | 5100 | 0.0009 | 0.0541 | 0.7870 | - |
| 1.8813 | 5200 | 0.0009 | 0.0594 | 0.7989 | - |
| 1.9175 | 5300 | 0.001 | 0.0558 | 0.8000 | - |
| 1.9537 | 5400 | 0.0009 | 0.0538 | 0.7905 | - |
| 1.9899 | 5500 | 0.0008 | 0.0555 | 0.7944 | - |
| 2.0260 | 5600 | 0.0009 | 0.0557 | 0.8127 | - |
| 2.0622 | 5700 | 0.0007 | 0.0542 | 0.8146 | - |
| 2.0984 | 5800 | 0.0008 | 0.0517 | 0.7990 | - |
| 2.1346 | 5900 | 0.0009 | 0.0500 | 0.8051 | - |
| 2.1708 | 6000 | 0.0009 | 0.0521 | 0.8019 | - |
| 2.2069 | 6100 | 0.0009 | 0.0511 | 0.8101 | - |
| 2.2431 | 6200 | 0.0008 | 0.0578 | 0.8087 | - |
| 2.2793 | 6300 | 0.0008 | 0.0585 | 0.8012 | - |
| 2.3155 | 6400 | 0.0008 | 0.0566 | 0.8083 | - |
| 2.3517 | 6500 | 0.0007 | 0.0535 | 0.8036 | - |
| 2.3878 | 6600 | 0.0008 | 0.0531 | 0.7988 | - |
| 2.4240 | 6700 | 0.0007 | 0.0574 | 0.8102 | - |
| 2.4602 | 6800 | 0.0007 | 0.0566 | 0.7944 | - |
| 2.4964 | 6900 | 0.0008 | 0.0528 | 0.8058 | - |
| 2.5326 | 7000 | 0.0007 | 0.0528 | 0.8056 | - |
| 2.5687 | 7100 | 0.0007 | 0.0506 | 0.8002 | - |
| 2.6049 | 7200 | 0.0007 | 0.0526 | 0.8038 | - |
| 2.6411 | 7300 | 0.0007 | 0.0554 | 0.8054 | - |
| 2.6773 | 7400 | 0.0007 | 0.0505 | 0.7928 | - |
| 2.7135 | 7500 | 0.0007 | 0.0505 | 0.8070 | - |
| 2.7496 | 7600 | 0.0007 | 0.0535 | 0.7977 | - |
| 2.7858 | 7700 | 0.0007 | 0.0536 | 0.8019 | - |
| 2.8220 | 7800 | 0.0006 | 0.0546 | 0.7989 | - |
| 2.8582 | 7900 | 0.0007 | 0.0543 | 0.8042 | - |
| 2.8944 | 8000 | 0.0007 | 0.0542 | 0.8105 | - |
| 2.9305 | 8100 | 0.0007 | 0.0541 | 0.8053 | - |
| 2.9667 | 8200 | 0.0007 | 0.0545 | 0.8135 | - |
| 3.0029 | 8300 | 0.0007 | 0.0598 | 0.8201 | - |
| 3.0391 | 8400 | 0.0008 | 0.0558 | 0.8050 | - |
| 3.0753 | 8500 | 0.0007 | 0.0510 | 0.7965 | - |
| 3.1114 | 8600 | 0.0006 | 0.0564 | 0.8042 | - |
| 3.1476 | 8700 | 0.0006 | 0.0559 | 0.7932 | - |
| 3.1838 | 8800 | 0.0006 | 0.0529 | 0.8028 | - |
| 3.2200 | 8900 | 0.0006 | 0.0542 | 0.8142 | - |
| 3.2562 | 9000 | 0.0006 | 0.0532 | 0.8055 | - |
| 3.2923 | 9100 | 0.0006 | 0.0506 | 0.7930 | - |
| 3.3285 | 9200 | 0.0007 | 0.0542 | 0.7927 | - |
| 3.3647 | 9300 | 0.0006 | 0.0523 | 0.8033 | - |
| 3.4009 | 9400 | 0.0006 | 0.0530 | 0.8079 | - |
| 3.4370 | 9500 | 0.0006 | 0.0544 | 0.7977 | - |
| 3.4732 | 9600 | 0.0005 | 0.0515 | 0.8019 | - |
| 3.5094 | 9700 | 0.0006 | 0.0481 | 0.8037 | - |
| 3.5456 | 9800 | 0.0005 | 0.0557 | 0.8007 | - |
| 3.5818 | 9900 | 0.0006 | 0.0495 | 0.8087 | - |
| 3.6179 | 10000 | 0.0006 | 0.0555 | 0.7991 | - |
| 3.6541 | 10100 | 0.0005 | 0.0560 | 0.7973 | - |
| 3.6903 | 10200 | 0.0007 | 0.0581 | 0.7945 | - |
| 3.7265 | 10300 | 0.0006 | 0.0546 | 0.8098 | - |
| 3.7627 | 10400 | 0.0006 | 0.0539 | 0.8074 | - |
| 3.7988 | 10500 | 0.0005 | 0.0501 | 0.8051 | - |
| 3.8350 | 10600 | 0.0005 | 0.0531 | 0.8032 | - |
| 3.8712 | 10700 | 0.0005 | 0.0502 | 0.8077 | - |
| 3.9074 | 10800 | 0.0006 | 0.0537 | 0.8131 | - |
| 3.9436 | 10900 | 0.0005 | 0.0510 | 0.8115 | - |
| 3.9797 | 11000 | 0.0006 | 0.0525 | 0.8173 | - |
| 4.0159 | 11100 | 0.0005 | 0.0513 | 0.8106 | - |
| 4.0521 | 11200 | 0.0006 | 0.0594 | 0.8061 | - |
| 4.0883 | 11300 | 0.0005 | 0.0514 | 0.8150 | - |
| 4.1245 | 11400 | 0.0005 | 0.0537 | 0.8168 | - |
| 4.1606 | 11500 | 0.0005 | 0.0571 | 0.8176 | - |
| 4.1968 | 11600 | 0.0005 | 0.0546 | 0.8159 | - |
| 4.2330 | 11700 | 0.0005 | 0.0496 | 0.8115 | - |
| 4.2692 | 11800 | 0.0005 | 0.0526 | 0.8072 | - |
| 4.3054 | 11900 | 0.0005 | 0.0512 | 0.8081 | - |
| 4.3415 | 12000 | 0.0005 | 0.0517 | 0.8025 | - |
| 4.3777 | 12100 | 0.0005 | 0.0533 | 0.8128 | - |
| 4.4139 | 12200 | 0.0005 | 0.0501 | 0.8121 | - |
| 4.4501 | 12300 | 0.0005 | 0.0507 | 0.8079 | - |
| 4.4863 | 12400 | 0.0005 | 0.0501 | 0.8070 | - |
| 4.5224 | 12500 | 0.0004 | 0.0537 | 0.8019 | - |
| 4.5586 | 12600 | 0.0004 | 0.0541 | 0.8005 | - |
| 4.5948 | 12700 | 0.0005 | 0.0525 | 0.8117 | - |
| 4.6310 | 12800 | 0.0004 | 0.0523 | 0.8070 | - |
| 4.6671 | 12900 | 0.0005 | 0.0526 | 0.8099 | - |
| 4.7033 | 13000 | 0.0004 | 0.0518 | 0.8166 | - |
| 4.7395 | 13100 | 0.0004 | 0.0547 | 0.8129 | - |
| 4.7757 | 13200 | 0.0005 | 0.0523 | 0.8130 | - |
| 4.8119 | 13300 | 0.0004 | 0.0504 | 0.8129 | - |
| 4.8480 | 13400 | 0.0005 | 0.0539 | 0.8113 | - |
| 4.8842 | 13500 | 0.0004 | 0.0523 | 0.8169 | - |
| 4.9204 | 13600 | 0.0005 | 0.0521 | 0.8164 | - |
| 4.9566 | 13700 | 0.0004 | 0.0575 | 0.8115 | - |
| 4.9928 | 13800 | 0.0004 | 0.0538 | 0.8186 | - |
| 5.0289 | 13900 | 0.0004 | 0.0530 | 0.8095 | - |
| 5.0651 | 14000 | 0.0003 | 0.0537 | 0.8162 | - |
| 5.1013 | 14100 | 0.0004 | 0.0560 | 0.8112 | - |
| 5.1375 | 14200 | 0.0004 | 0.0528 | 0.8125 | - |
| 5.1737 | 14300 | 0.0004 | 0.0533 | 0.8137 | - |
| 5.2098 | 14400 | 0.0003 | 0.0537 | 0.8198 | - |
| 5.2460 | 14500 | 0.0004 | 0.0530 | 0.8102 | - |
| 5.2822 | 14600 | 0.0004 | 0.0562 | 0.8099 | - |
| 5.3184 | 14700 | 0.0004 | 0.0522 | 0.8084 | - |
| 5.3546 | 14800 | 0.0004 | 0.0515 | 0.8128 | - |
| 5.3907 | 14900 | 0.0004 | 0.0555 | 0.8107 | - |
| 5.4269 | 15000 | 0.0004 | 0.0533 | 0.8113 | - |
| 5.4631 | 15100 | 0.0003 | 0.0538 | 0.8135 | - |
| 5.4993 | 15200 | 0.0004 | 0.0552 | 0.8139 | - |
| 5.5355 | 15300 | 0.0003 | 0.0513 | 0.8102 | - |
| 5.5716 | 15400 | 0.0004 | 0.0542 | 0.8108 | - |
| 5.6078 | 15500 | 0.0003 | 0.0541 | 0.8041 | - |
| 5.6440 | 15600 | 0.0004 | 0.0512 | 0.8074 | - |
| 5.6802 | 15700 | 0.0003 | 0.0553 | 0.8100 | - |
| 5.7164 | 15800 | 0.0003 | 0.0539 | 0.8088 | - |
| 5.7525 | 15900 | 0.0004 | 0.0527 | 0.8094 | - |
| 5.7887 | 16000 | 0.0004 | 0.0524 | 0.8080 | - |
| 5.8249 | 16100 | 0.0003 | 0.0525 | 0.8112 | - |
| 5.8611 | 16200 | 0.0003 | 0.0537 | 0.8109 | - |
| 5.8973 | 16300 | 0.0003 | 0.0539 | 0.8129 | - |
| 5.9334 | 16400 | 0.0003 | 0.0543 | 0.8052 | - |
| 5.9696 | 16500 | 0.0003 | 0.0544 | 0.8093 | - |
| 6.0058 | 16600 | 0.0004 | 0.0532 | 0.8109 | - |
| 6.0420 | 16700 | 0.0002 | 0.0558 | 0.8108 | - |
| 6.0781 | 16800 | 0.0002 | 0.0529 | 0.8089 | - |
| 6.1143 | 16900 | 0.0003 | 0.0539 | 0.8074 | - |
| 6.1505 | 17000 | 0.0003 | 0.0534 | 0.8118 | - |
| 6.1867 | 17100 | 0.0003 | 0.0539 | 0.8048 | - |
| 6.2229 | 17200 | 0.0003 | 0.0537 | 0.8049 | - |
| 6.2590 | 17300 | 0.0003 | 0.0553 | 0.8102 | - |
| 6.2952 | 17400 | 0.0002 | 0.0533 | 0.8053 | - |
| 6.3314 | 17500 | 0.0003 | 0.0550 | 0.8071 | - |
| 6.3676 | 17600 | 0.0002 | 0.0530 | 0.8128 | - |
| 6.4038 | 17700 | 0.0003 | 0.0547 | 0.8159 | - |
| 6.4399 | 17800 | 0.0002 | 0.0539 | 0.8120 | - |
| 6.4761 | 17900 | 0.0003 | 0.0540 | 0.8107 | - |
| 6.5123 | 18000 | 0.0003 | 0.0535 | 0.8069 | - |
| 6.5485 | 18100 | 0.0003 | 0.0541 | 0.8129 | - |
| 6.5847 | 18200 | 0.0003 | 0.0522 | 0.8132 | - |
| 6.6208 | 18300 | 0.0002 | 0.0539 | 0.8135 | - |
| 6.6570 | 18400 | 0.0002 | 0.0542 | 0.8142 | - |
| 6.6932 | 18500 | 0.0003 | 0.0529 | 0.8101 | - |
| 6.7294 | 18600 | 0.0003 | 0.0533 | 0.8073 | - |
| 6.7656 | 18700 | 0.0003 | 0.0525 | 0.8095 | - |
| 6.8017 | 18800 | 0.0003 | 0.0534 | 0.8089 | - |
| 6.8379 | 18900 | 0.0002 | 0.0519 | 0.8134 | - |
| 6.8741 | 19000 | 0.0002 | 0.0536 | 0.8141 | - |
| 6.9103 | 19100 | 0.0002 | 0.0535 | 0.8115 | - |
| 6.9465 | 19200 | 0.0002 | 0.0519 | 0.8107 | - |
| 6.9826 | 19300 | 0.0002 | 0.0546 | 0.8093 | - |
| 7.0188 | 19400 | 0.0002 | 0.0532 | 0.8112 | - |
| 7.0550 | 19500 | 0.0002 | 0.0526 | 0.8145 | - |
| 7.0912 | 19600 | 0.0002 | 0.0529 | 0.8111 | - |
| 7.1274 | 19700 | 0.0002 | 0.0540 | 0.8090 | - |
| 7.1635 | 19800 | 0.0002 | 0.0525 | 0.8116 | - |
| 7.1997 | 19900 | 0.0002 | 0.0534 | 0.8115 | - |
| 7.2359 | 20000 | 0.0002 | 0.0526 | 0.8123 | - |
| 7.2721 | 20100 | 0.0002 | 0.0524 | 0.8143 | - |
| 7.3082 | 20200 | 0.0002 | 0.0526 | 0.8059 | - |
| 7.3444 | 20300 | 0.0002 | 0.0535 | 0.8091 | - |
| 7.3806 | 20400 | 0.0002 | 0.0532 | 0.8094 | - |
| 7.4168 | 20500 | 0.0002 | 0.0529 | 0.8108 | - |
| 7.4530 | 20600 | 0.0002 | 0.0542 | 0.8108 | - |
| 7.4891 | 20700 | 0.0002 | 0.0525 | 0.8102 | - |
| 7.5253 | 20800 | 0.0002 | 0.0541 | 0.8106 | - |
| 7.5615 | 20900 | 0.0002 | 0.0538 | 0.8095 | - |
| 7.5977 | 21000 | 0.0003 | 0.0523 | 0.8136 | - |
| 7.6339 | 21100 | 0.0002 | 0.0544 | 0.8108 | - |
| 7.6700 | 21200 | 0.0002 | 0.0525 | 0.8090 | - |
| 7.7062 | 21300 | 0.0002 | 0.0528 | 0.8108 | - |
| 7.7424 | 21400 | 0.0002 | 0.0531 | 0.8115 | - |
| 7.7786 | 21500 | 0.0002 | 0.0541 | 0.8107 | - |
| 7.8148 | 21600 | 0.0001 | 0.0525 | 0.8117 | - |
| 7.8509 | 21700 | 0.0002 | 0.0534 | 0.8115 | - |
| 7.8871 | 21800 | 0.0002 | 0.0541 | 0.8105 | - |
| 7.9233 | 21900 | 0.0002 | 0.0538 | 0.8094 | - |
| 7.9595 | 22000 | 0.0002 | 0.0530 | 0.8106 | - |
| 7.9957 | 22100 | 0.0002 | 0.0527 | 0.8104 | - |
| 8.0318 | 22200 | 0.0001 | 0.0534 | 0.8098 | - |
| 8.0680 | 22300 | 0.0002 | 0.0537 | 0.8090 | - |
| 8.1042 | 22400 | 0.0001 | 0.0533 | 0.8103 | - |
| 8.1404 | 22500 | 0.0002 | 0.0528 | 0.8099 | - |
| 8.1766 | 22600 | 0.0001 | 0.0531 | 0.8106 | - |
| 8.2127 | 22700 | 0.0001 | 0.0534 | 0.8116 | - |
| 8.2489 | 22800 | 0.0001 | 0.0538 | 0.8102 | - |
| 8.2851 | 22900 | 0.0001 | 0.0530 | 0.8108 | - |
| 8.3213 | 23000 | 0.0002 | 0.0529 | 0.8112 | - |
| 8.3575 | 23100 | 0.0001 | 0.0533 | 0.8099 | - |
| 8.3936 | 23200 | 0.0001 | 0.0534 | 0.8107 | - |
| 8.4298 | 23300 | 0.0002 | 0.0535 | 0.8110 | - |
| 8.4660 | 23400 | 0.0001 | 0.0543 | 0.8108 | - |
| 8.5022 | 23500 | 0.0001 | 0.0530 | 0.8119 | - |
| 8.5384 | 23600 | 0.0001 | 0.0530 | 0.8132 | - |
| 8.5745 | 23700 | 0.0001 | 0.0531 | 0.8128 | - |
| 8.6107 | 23800 | 0.0002 | 0.0532 | 0.8119 | - |
| 8.6469 | 23900 | 0.0002 | 0.0531 | 0.8120 | - |
| 8.6831 | 24000 | 0.0001 | 0.0531 | 0.8121 | - |
| 8.7192 | 24100 | 0.0001 | 0.0525 | 0.8134 | - |
| 8.7554 | 24200 | 0.0002 | 0.0524 | 0.8133 | - |
| 8.7916 | 24300 | 0.0001 | 0.0535 | 0.8141 | - |
| 8.8278 | 24400 | 0.0002 | 0.0529 | 0.8118 | - |
| 8.8640 | 24500 | 0.0001 | 0.0529 | 0.8115 | - |
| 8.9001 | 24600 | 0.0001 | 0.0528 | 0.8127 | - |
| 8.9363 | 24700 | 0.0002 | 0.0527 | 0.8111 | - |
| 8.9725 | 24800 | 0.0001 | 0.0536 | 0.8114 | - |
| 9.0087 | 24900 | 0.0001 | 0.0531 | 0.8124 | - |
| 9.0449 | 25000 | 0.0001 | 0.0532 | 0.8123 | - |
| 9.0810 | 25100 | 0.0001 | 0.0534 | 0.8130 | - |
| 9.1172 | 25200 | 0.0001 | 0.0533 | 0.8121 | - |
| 9.1534 | 25300 | 0.0002 | 0.0534 | 0.8119 | - |
| 9.1896 | 25400 | 0.0001 | 0.0532 | 0.8118 | - |
| 9.2258 | 25500 | 0.0001 | 0.0532 | 0.8112 | - |
| 9.2619 | 25600 | 0.0001 | 0.0532 | 0.8121 | - |
| 9.2981 | 25700 | 0.0002 | 0.0537 | 0.8120 | - |
| 9.3343 | 25800 | 0.0001 | 0.0535 | 0.8127 | - |
| 9.3705 | 25900 | 0.0001 | 0.0529 | 0.8133 | - |
| 9.4067 | 26000 | 0.0001 | 0.0529 | 0.8138 | - |
| 9.4428 | 26100 | 0.0001 | 0.0534 | 0.8131 | - |
| 9.4790 | 26200 | 0.0001 | 0.0529 | 0.8137 | - |
| 9.5152 | 26300 | 0.0002 | 0.0529 | 0.8135 | - |
| 9.5514 | 26400 | 0.0001 | 0.0528 | 0.8129 | - |
| 9.5876 | 26500 | 0.0001 | 0.0530 | 0.8124 | - |
| 9.6237 | 26600 | 0.0001 | 0.0529 | 0.8132 | - |
| 9.6599 | 26700 | 0.0001 | 0.0530 | 0.8128 | - |
| 9.6961 | 26800 | 0.0001 | 0.0530 | 0.8132 | - |
| 9.7323 | 26900 | 0.0001 | 0.0529 | 0.8129 | - |
| 9.7685 | 27000 | 0.0002 | 0.0528 | 0.8131 | - |
| 9.8046 | 27100 | 0.0001 | 0.0529 | 0.8131 | - |
| 9.8408 | 27200 | 0.0002 | 0.0531 | 0.8128 | - |
| 9.8770 | 27300 | 0.0001 | 0.0532 | 0.8130 | - |
| 9.9132 | 27400 | 0.0001 | 0.0531 | 0.8129 | - |
| 9.9493 | 27500 | 0.0001 | 0.0531 | 0.8129 | - |
| 9.9855 | 27600 | 0.0001 | 0.0531 | 0.8130 | - |
| -1 | -1 | - | - | - | 0.7644 |
</details>
### Framework Versions
- Python: 3.10.16
- Sentence Transformers: 3.4.1
- Transformers: 4.49.0
- PyTorch: 2.5.1+cu124
- Accelerate: 0.34.2
- Datasets: 2.19.2
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```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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