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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1000000
- loss:TripletLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: '|Evaluation procedure (procedure)| : { |Method (attribute)| =
|Evaluation - action (qualifier value)|, |Has specimen (attribute)| = |Urine specimen
(specimen)| }'
sentences:
- Evaluation of urine specimen (procedure)
- Tacrolimus-containing product in oral dose form (medicinal product form)
- '|Measurement of substance in specimen (procedure)| + |Organic acids measurement
(procedure)| : { |Method (attribute)| = |Measurement - action (qualifier value)|,
|Component (attribute)| = |Organic acid (substance)|, |Has specimen (attribute)|
= |Fluid specimen (specimen)| }'
- source_sentence: Allergy to meclozine (finding)
sentences:
- Meclizine allergy (finding)
- Allergy to quinidine (finding)
- Disorder of ovary (disorder)
- source_sentence: '|Specimen observable (observable entity)|'
sentences:
- Carbamide peroxide (product)
- Neoplasm of uncertain behavior of dome of urinary bladder (disorder)
- Microscopic specimen observable (observable entity)
- source_sentence: '|Dilation of esophagus (procedure)| : { |Method (attribute)| =
|Dilation - action (qualifier value)|, |Procedure site - Direct (attribute)| =
|Esophageal structure (body structure)|, |Direct morphology (attribute)| = |Stricture
(morphologic abnormality)| }'
sentences:
- Fissure for ligamentum teres of liver (body structure)
- Dilatation of esophageal stricture (procedure)
- Dilation and insertion of tube into esophagus (procedure)
- source_sentence: '|Estradiol and/or estradiol derivative (substance)| + |Steroid
hormone (substance)| + |Substance with estrogen receptor agonist mechanism of
action (substance)| : |Has disposition (attribute)| = |Estrogen receptor agonist
(disposition)|, '
sentences:
- Oral form dioctahedral smectite (medicinal product form)
- 17-Beta oestradiol (substance)
- Rupture of Descemet's membrane of right eye (disorder)
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
results:
- task:
type: triplet
name: Triplet
dataset:
name: snomed triplet 1M 3 4 3 dev
type: snomed_triplet_1M_3_4_3-dev
metrics:
- type: cosine_accuracy
value: 0.979325
name: Cosine Accuracy
- type: cosine_accuracy
value: 0.9780125
name: Cosine Accuracy
---
# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the parquet 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/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- parquet
<!-- - **Language:** Unknown -->
<!-- - **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': 256, '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})
(2): Normalize()
)
```
## 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("yyzheng00/snomed_triplet_1M")
# Run inference
sentences = [
'|Estradiol and/or estradiol derivative (substance)| + |Steroid hormone (substance)| + |Substance with estrogen receptor agonist mechanism of action (substance)| : |Has disposition (attribute)| = |Estrogen receptor agonist (disposition)|, ',
'17-Beta oestradiol (substance)',
"Rupture of Descemet's membrane of right eye (disorder)",
]
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]
```
<!--
### 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
#### Triplet
* Dataset: `snomed_triplet_1M_3_4_3-dev`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.9793** |
#### Triplet
* Dataset: `snomed_triplet_1M_3_4_3-dev`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:--------------------|:----------|
| **cosine_accuracy** | **0.978** |
<!--
## 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
#### parquet
* Dataset: parquet
* Size: 1,000,000 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 50.47 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.36 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 22.41 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| <code>Anas versicolor (organism)</code> | <code>Silver teal (organism)</code> | <code>Cryotherapy of gastric lesion (procedure)</code> |
| <code>|Vitamin B2 and/or vitamin B2 derivative (substance)| : |Is modification of (attribute)| = |Riboflavin (substance)|, </code> | <code>Riboflavin sodium phosphate (substance)</code> | <code>Nicotinic acid (substance)</code> |
| <code>|Aplasia of distal phalanx of fifth toe (disorder)| + |Disorder of epiphysis (disorder)| : { |Occurrence (attribute)| = |Congenital (qualifier value)|, |Finding site (attribute)| = |Entire epiphysis of distal phalanx of fifth toe (body structure)|, |Associated morphology (attribute)| = |Agenesis (morphologic abnormality)|, |Pathological process (attribute)| = |Pathological developmental process (qualifier value)| }</code> | <code>Agenesis of epiphysis of distal phalanx of fifth toe (disorder)</code> | <code>Product containing mianserin in oral dose form (medicinal product form)</code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"distance_metric": "TripletDistanceMetric.COSINE",
"triplet_margin": 0.2
}
```
### Evaluation Dataset
#### parquet
* Dataset: parquet
* Size: 1,000,000 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 48.58 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.51 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 20.96 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>|Genus Roseateles (organism)|</code> | <code>Pelomonas saccharophila (organism)</code> | <code>|Mycology culture (procedure)|:{|Component (attribute)|=|Pichia manshurica (organism)|}{|Has specimen (attribute)|=|Fluid specimen (specimen)|}</code> |
| <code>|Partial urinary cystectomy (procedure)| + |Procedure on neck of urinary bladder (procedure)| + |Surgical procedure on outlet of urinary bladder (procedure)| + |Transurethral excision of urinary bladder (procedure)| : { |Surgical approach (attribute)| = |Transurethral approach (qualifier value)|, |Method (attribute)| = |Excision - action (qualifier value)|, |Procedure site - Direct (attribute)| = |Structure of neck of urinary bladder (body structure)| }</code> | <code>Transurethral excision of neck of urinary bladder (procedure)</code> | <code>Paracentesis of urinary bladder (procedure)</code> |
| <code>|Product containing integrase strand transfer inhibitor (product)| + |Product containing nitrogen and/or nitrogen compound (product)| : { |Has active ingredient (attribute)| = |Dolutegravir (substance)| }</code> | <code>Dolutegravir (product)</code> | <code>Product containing ammonium bicarbonate (medicinal product)</code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"distance_metric": "TripletDistanceMetric.COSINE",
"triplet_margin": 0.2
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### 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`: 16
- `per_device_eval_batch_size`: 16
- `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`: 1
- `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`: 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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss | snomed_triplet_1M_3_4_3-dev_cosine_accuracy |
|:------:|:-----:|:-------------:|:---------------:|:-------------------------------------------:|
| 0.0027 | 100 | 0.0553 | 0.0405 | 0.9199 |
| 0.0053 | 200 | 0.0412 | 0.0316 | 0.9369 |
| 0.008 | 300 | 0.0277 | 0.0296 | 0.9405 |
| 0.0107 | 400 | 0.0303 | 0.0282 | 0.9433 |
| 0.0133 | 500 | 0.0262 | 0.0275 | 0.9450 |
| 0.016 | 600 | 0.0293 | 0.0266 | 0.9466 |
| 0.0187 | 700 | 0.0301 | 0.0257 | 0.9480 |
| 0.0213 | 800 | 0.0262 | 0.0249 | 0.9506 |
| 0.024 | 900 | 0.0258 | 0.0240 | 0.9527 |
| 0.0267 | 1000 | 0.0286 | 0.0235 | 0.9537 |
| 0.0293 | 1100 | 0.0239 | 0.0229 | 0.9547 |
| 0.032 | 1200 | 0.0211 | 0.0231 | 0.9548 |
| 0.0347 | 1300 | 0.0235 | 0.0228 | 0.9555 |
| 0.0373 | 1400 | 0.0257 | 0.0225 | 0.9559 |
| 0.04 | 1500 | 0.025 | 0.0217 | 0.9572 |
| 0.0427 | 1600 | 0.0216 | 0.0214 | 0.9581 |
| 0.0453 | 1700 | 0.0247 | 0.0214 | 0.9580 |
| 0.048 | 1800 | 0.0229 | 0.0212 | 0.9588 |
| 0.0507 | 1900 | 0.0207 | 0.0211 | 0.9585 |
| 0.0533 | 2000 | 0.0224 | 0.0214 | 0.9585 |
| 0.056 | 2100 | 0.0237 | 0.0209 | 0.9587 |
| 0.0587 | 2200 | 0.0205 | 0.0205 | 0.9591 |
| 0.0613 | 2300 | 0.0218 | 0.0208 | 0.9590 |
| 0.064 | 2400 | 0.0209 | 0.0204 | 0.9601 |
| 0.0667 | 2500 | 0.0225 | 0.0207 | 0.9591 |
| 0.0693 | 2600 | 0.021 | 0.0206 | 0.9604 |
| 0.072 | 2700 | 0.0222 | 0.0197 | 0.9622 |
| 0.0747 | 2800 | 0.0214 | 0.0198 | 0.9615 |
| 0.0773 | 2900 | 0.0204 | 0.0200 | 0.9611 |
| 0.08 | 3000 | 0.026 | 0.0197 | 0.9622 |
| 0.0827 | 3100 | 0.0181 | 0.0197 | 0.9617 |
| 0.0853 | 3200 | 0.023 | 0.0195 | 0.9612 |
| 0.088 | 3300 | 0.0198 | 0.0195 | 0.9620 |
| 0.0907 | 3400 | 0.0205 | 0.0198 | 0.9611 |
| 0.0933 | 3500 | 0.0208 | 0.0194 | 0.9622 |
| 0.096 | 3600 | 0.0205 | 0.0205 | 0.9592 |
| 0.0987 | 3700 | 0.0242 | 0.0196 | 0.9619 |
| 0.1013 | 3800 | 0.0178 | 0.0191 | 0.9634 |
| 0.104 | 3900 | 0.0189 | 0.0189 | 0.9629 |
| 0.1067 | 4000 | 0.0249 | 0.0188 | 0.9637 |
| 0.1093 | 4100 | 0.0201 | 0.0186 | 0.9634 |
| 0.112 | 4200 | 0.0198 | 0.0185 | 0.9636 |
| 0.1147 | 4300 | 0.0208 | 0.0186 | 0.9639 |
| 0.1173 | 4400 | 0.019 | 0.0185 | 0.9639 |
| 0.12 | 4500 | 0.0203 | 0.0188 | 0.9638 |
| 0.1227 | 4600 | 0.0205 | 0.0191 | 0.9633 |
| 0.1253 | 4700 | 0.0183 | 0.0194 | 0.9623 |
| 0.128 | 4800 | 0.022 | 0.0183 | 0.9643 |
| 0.1307 | 4900 | 0.0193 | 0.0182 | 0.9649 |
| 0.1333 | 5000 | 0.0192 | 0.0178 | 0.9659 |
| 0.136 | 5100 | 0.0212 | 0.0185 | 0.9650 |
| 0.1387 | 5200 | 0.0181 | 0.0183 | 0.9639 |
| 0.1413 | 5300 | 0.0189 | 0.0177 | 0.9656 |
| 0.144 | 5400 | 0.0209 | 0.0179 | 0.9658 |
| 0.1467 | 5500 | 0.0216 | 0.0175 | 0.9665 |
| 0.1493 | 5600 | 0.0178 | 0.0176 | 0.9665 |
| 0.152 | 5700 | 0.019 | 0.0178 | 0.9658 |
| 0.1547 | 5800 | 0.0215 | 0.0180 | 0.9655 |
| 0.1573 | 5900 | 0.0194 | 0.0176 | 0.9663 |
| 0.16 | 6000 | 0.0182 | 0.0181 | 0.9651 |
| 0.1627 | 6100 | 0.0186 | 0.0185 | 0.9640 |
| 0.1653 | 6200 | 0.019 | 0.0178 | 0.9650 |
| 0.168 | 6300 | 0.019 | 0.0172 | 0.9667 |
| 0.1707 | 6400 | 0.0186 | 0.0178 | 0.9654 |
| 0.1733 | 6500 | 0.0192 | 0.0172 | 0.9669 |
| 0.176 | 6600 | 0.0185 | 0.0171 | 0.9670 |
| 0.1787 | 6700 | 0.019 | 0.0169 | 0.9674 |
| 0.1813 | 6800 | 0.0183 | 0.0170 | 0.9671 |
| 0.184 | 6900 | 0.0199 | 0.0168 | 0.9675 |
| 0.1867 | 7000 | 0.0186 | 0.0169 | 0.9673 |
| 0.1893 | 7100 | 0.016 | 0.0169 | 0.9676 |
| 0.192 | 7200 | 0.0158 | 0.0174 | 0.9663 |
| 0.1947 | 7300 | 0.0205 | 0.0169 | 0.9681 |
| 0.1973 | 7400 | 0.0189 | 0.0169 | 0.9669 |
| 0.2 | 7500 | 0.0188 | 0.0170 | 0.9672 |
| 0.2027 | 7600 | 0.0193 | 0.0168 | 0.9674 |
| 0.2053 | 7700 | 0.0202 | 0.0168 | 0.9673 |
| 0.208 | 7800 | 0.0184 | 0.0165 | 0.9676 |
| 0.2107 | 7900 | 0.0196 | 0.0162 | 0.9687 |
| 0.2133 | 8000 | 0.0186 | 0.0161 | 0.9688 |
| 0.216 | 8100 | 0.0174 | 0.0166 | 0.9670 |
| 0.2187 | 8200 | 0.0178 | 0.0166 | 0.9676 |
| 0.2213 | 8300 | 0.0187 | 0.0172 | 0.9664 |
| 0.224 | 8400 | 0.0175 | 0.0162 | 0.9685 |
| 0.2267 | 8500 | 0.0165 | 0.0163 | 0.9674 |
| 0.2293 | 8600 | 0.018 | 0.0164 | 0.9678 |
| 0.232 | 8700 | 0.0192 | 0.0165 | 0.9680 |
| 0.2347 | 8800 | 0.0182 | 0.0164 | 0.9680 |
| 0.2373 | 8900 | 0.0191 | 0.0162 | 0.9689 |
| 0.24 | 9000 | 0.0173 | 0.0161 | 0.9683 |
| 0.2427 | 9100 | 0.022 | 0.0159 | 0.9685 |
| 0.2453 | 9200 | 0.0182 | 0.0161 | 0.9685 |
| 0.248 | 9300 | 0.0174 | 0.0165 | 0.9684 |
| 0.2507 | 9400 | 0.0181 | 0.0168 | 0.9667 |
| 0.2533 | 9500 | 0.0159 | 0.0163 | 0.9684 |
| 0.256 | 9600 | 0.0176 | 0.0162 | 0.9685 |
| 0.2587 | 9700 | 0.0155 | 0.0170 | 0.9668 |
| 0.2613 | 9800 | 0.0183 | 0.0162 | 0.9679 |
| 0.264 | 9900 | 0.0183 | 0.0156 | 0.9693 |
| 0.2667 | 10000 | 0.019 | 0.0156 | 0.9695 |
| 0.2693 | 10100 | 0.0167 | 0.0162 | 0.9683 |
| 0.272 | 10200 | 0.0202 | 0.0156 | 0.9695 |
| 0.2747 | 10300 | 0.0174 | 0.0157 | 0.9694 |
| 0.2773 | 10400 | 0.0165 | 0.0155 | 0.9694 |
| 0.28 | 10500 | 0.0176 | 0.0155 | 0.9700 |
| 0.2827 | 10600 | 0.0181 | 0.0153 | 0.9699 |
| 0.2853 | 10700 | 0.0184 | 0.0154 | 0.9697 |
| 0.288 | 10800 | 0.0172 | 0.0155 | 0.9692 |
| 0.2907 | 10900 | 0.0153 | 0.0156 | 0.9694 |
| 0.2933 | 11000 | 0.0169 | 0.0154 | 0.9700 |
| 0.296 | 11100 | 0.0181 | 0.0153 | 0.9698 |
| 0.2987 | 11200 | 0.0164 | 0.0154 | 0.9700 |
| 0.3013 | 11300 | 0.0177 | 0.0158 | 0.9691 |
| 0.304 | 11400 | 0.0154 | 0.0153 | 0.9700 |
| 0.3067 | 11500 | 0.0159 | 0.0153 | 0.9700 |
| 0.3093 | 11600 | 0.0162 | 0.0152 | 0.9699 |
| 0.312 | 11700 | 0.0172 | 0.0150 | 0.9710 |
| 0.3147 | 11800 | 0.0151 | 0.0153 | 0.9696 |
| 0.3173 | 11900 | 0.0157 | 0.0153 | 0.9697 |
| 0.32 | 12000 | 0.0145 | 0.0150 | 0.9705 |
| 0.3227 | 12100 | 0.0184 | 0.0153 | 0.9701 |
| 0.3253 | 12200 | 0.0173 | 0.0151 | 0.9706 |
| 0.328 | 12300 | 0.0158 | 0.0151 | 0.971 |
| 0.3307 | 12400 | 0.0154 | 0.0154 | 0.9697 |
| 0.3333 | 12500 | 0.0126 | 0.0153 | 0.9697 |
| 0.336 | 12600 | 0.0151 | 0.0150 | 0.9704 |
| 0.3387 | 12700 | 0.0152 | 0.0152 | 0.9698 |
| 0.3413 | 12800 | 0.0176 | 0.0150 | 0.9707 |
| 0.344 | 12900 | 0.0172 | 0.0149 | 0.9705 |
| 0.3467 | 13000 | 0.0149 | 0.0151 | 0.9704 |
| 0.3493 | 13100 | 0.0154 | 0.0151 | 0.9701 |
| 0.352 | 13200 | 0.0138 | 0.0148 | 0.9705 |
| 0.3547 | 13300 | 0.0195 | 0.0149 | 0.9705 |
| 0.3573 | 13400 | 0.0162 | 0.0151 | 0.9707 |
| 0.36 | 13500 | 0.0137 | 0.0150 | 0.9708 |
| 0.3627 | 13600 | 0.0153 | 0.0151 | 0.9704 |
| 0.3653 | 13700 | 0.0143 | 0.0150 | 0.9705 |
| 0.368 | 13800 | 0.0161 | 0.0149 | 0.9709 |
| 0.3707 | 13900 | 0.0136 | 0.0149 | 0.9712 |
| 0.3733 | 14000 | 0.0161 | 0.0150 | 0.9709 |
| 0.376 | 14100 | 0.0171 | 0.0148 | 0.9718 |
| 0.3787 | 14200 | 0.0168 | 0.0147 | 0.9717 |
| 0.3813 | 14300 | 0.0159 | 0.0147 | 0.9718 |
| 0.384 | 14400 | 0.0167 | 0.0145 | 0.9721 |
| 0.3867 | 14500 | 0.0158 | 0.0147 | 0.9715 |
| 0.3893 | 14600 | 0.0153 | 0.0146 | 0.9713 |
| 0.392 | 14700 | 0.0131 | 0.0145 | 0.9717 |
| 0.3947 | 14800 | 0.0166 | 0.0144 | 0.9722 |
| 0.3973 | 14900 | 0.0164 | 0.0142 | 0.9720 |
| 0.4 | 15000 | 0.0166 | 0.0143 | 0.9720 |
| 0.4027 | 15100 | 0.0168 | 0.0143 | 0.9726 |
| 0.4053 | 15200 | 0.0145 | 0.0143 | 0.9723 |
| 0.408 | 15300 | 0.0149 | 0.0144 | 0.9717 |
| 0.4107 | 15400 | 0.0152 | 0.0141 | 0.9729 |
| 0.4133 | 15500 | 0.0147 | 0.0140 | 0.9734 |
| 0.416 | 15600 | 0.0141 | 0.0140 | 0.9731 |
| 0.4187 | 15700 | 0.0147 | 0.0140 | 0.9731 |
| 0.4213 | 15800 | 0.0158 | 0.0139 | 0.9734 |
| 0.424 | 15900 | 0.0177 | 0.0141 | 0.9728 |
| 0.4267 | 16000 | 0.0151 | 0.0137 | 0.9734 |
| 0.4293 | 16100 | 0.0148 | 0.0145 | 0.9724 |
| 0.432 | 16200 | 0.0135 | 0.0144 | 0.9721 |
| 0.4347 | 16300 | 0.0167 | 0.0138 | 0.9736 |
| 0.4373 | 16400 | 0.0153 | 0.0138 | 0.9739 |
| 0.44 | 16500 | 0.014 | 0.0139 | 0.9731 |
| 0.4427 | 16600 | 0.0168 | 0.0139 | 0.9734 |
| 0.4453 | 16700 | 0.0125 | 0.0139 | 0.9734 |
| 0.448 | 16800 | 0.0163 | 0.0139 | 0.9733 |
| 0.4507 | 16900 | 0.0179 | 0.0137 | 0.9742 |
| 0.4533 | 17000 | 0.0162 | 0.0136 | 0.9738 |
| 0.456 | 17100 | 0.0148 | 0.0137 | 0.9734 |
| 0.4587 | 17200 | 0.0154 | 0.0137 | 0.9737 |
| 0.4613 | 17300 | 0.0178 | 0.0139 | 0.9732 |
| 0.464 | 17400 | 0.0176 | 0.0138 | 0.9731 |
| 0.4667 | 17500 | 0.012 | 0.0135 | 0.9738 |
| 0.4693 | 17600 | 0.0136 | 0.0137 | 0.9731 |
| 0.472 | 17700 | 0.0156 | 0.0133 | 0.9740 |
| 0.4747 | 17800 | 0.0151 | 0.0136 | 0.9738 |
| 0.4773 | 17900 | 0.0145 | 0.0135 | 0.9741 |
| 0.48 | 18000 | 0.0176 | 0.0136 | 0.9735 |
| 0.4827 | 18100 | 0.0143 | 0.0133 | 0.9744 |
| 0.4853 | 18200 | 0.0144 | 0.0133 | 0.9742 |
| 0.488 | 18300 | 0.0139 | 0.0135 | 0.9738 |
| 0.4907 | 18400 | 0.0134 | 0.0134 | 0.9740 |
| 0.4933 | 18500 | 0.0135 | 0.0134 | 0.9738 |
| 0.496 | 18600 | 0.0144 | 0.0134 | 0.9738 |
| 0.4987 | 18700 | 0.0143 | 0.0135 | 0.9744 |
| 0.5013 | 18800 | 0.0165 | 0.0133 | 0.9748 |
| 0.504 | 18900 | 0.0147 | 0.0133 | 0.9742 |
| 0.5067 | 19000 | 0.0159 | 0.0133 | 0.9743 |
| 0.5093 | 19100 | 0.013 | 0.0132 | 0.9746 |
| 0.512 | 19200 | 0.0145 | 0.0133 | 0.9744 |
| 0.5147 | 19300 | 0.0147 | 0.0134 | 0.9743 |
| 0.5173 | 19400 | 0.0151 | 0.0131 | 0.9748 |
| 0.52 | 19500 | 0.0134 | 0.0132 | 0.9742 |
| 0.5227 | 19600 | 0.0148 | 0.0135 | 0.9740 |
| 0.5253 | 19700 | 0.0142 | 0.0134 | 0.9744 |
| 0.528 | 19800 | 0.0158 | 0.0132 | 0.9746 |
| 0.5307 | 19900 | 0.015 | 0.0134 | 0.9748 |
| 0.5333 | 20000 | 0.0146 | 0.0132 | 0.9745 |
| 0.536 | 20100 | 0.0136 | 0.0130 | 0.9752 |
| 0.5387 | 20200 | 0.0142 | 0.0131 | 0.9750 |
| 0.5413 | 20300 | 0.0137 | 0.0130 | 0.9749 |
| 0.544 | 20400 | 0.0118 | 0.0132 | 0.9741 |
| 0.5467 | 20500 | 0.0129 | 0.0131 | 0.9750 |
| 0.5493 | 20600 | 0.015 | 0.0131 | 0.9749 |
| 0.552 | 20700 | 0.0154 | 0.0132 | 0.9743 |
| 0.5547 | 20800 | 0.0165 | 0.0132 | 0.9747 |
| 0.5573 | 20900 | 0.0158 | 0.0131 | 0.9751 |
| 0.56 | 21000 | 0.014 | 0.0130 | 0.9746 |
| 0.5627 | 21100 | 0.0157 | 0.0129 | 0.9755 |
| 0.5653 | 21200 | 0.014 | 0.0129 | 0.9754 |
| 0.568 | 21300 | 0.0149 | 0.0129 | 0.9751 |
| 0.5707 | 21400 | 0.0114 | 0.0129 | 0.9754 |
| 0.5733 | 21500 | 0.0116 | 0.0128 | 0.9755 |
| 0.576 | 21600 | 0.0114 | 0.0132 | 0.9743 |
| 0.5787 | 21700 | 0.0164 | 0.0127 | 0.9759 |
| 0.5813 | 21800 | 0.0137 | 0.0127 | 0.9754 |
| 0.584 | 21900 | 0.0118 | 0.0129 | 0.9745 |
| 0.5867 | 22000 | 0.0126 | 0.0129 | 0.9752 |
| 0.5893 | 22100 | 0.0153 | 0.0126 | 0.9758 |
| 0.592 | 22200 | 0.0128 | 0.0126 | 0.9759 |
| 0.5947 | 22300 | 0.0161 | 0.0128 | 0.9755 |
| 0.5973 | 22400 | 0.0121 | 0.0128 | 0.9754 |
| 0.6 | 22500 | 0.0144 | 0.0126 | 0.9758 |
| 0.6027 | 22600 | 0.0138 | 0.0127 | 0.9754 |
| 0.6053 | 22700 | 0.0114 | 0.0125 | 0.9757 |
| 0.608 | 22800 | 0.0163 | 0.0126 | 0.9755 |
| 0.6107 | 22900 | 0.0127 | 0.0125 | 0.9757 |
| 0.6133 | 23000 | 0.0139 | 0.0126 | 0.9752 |
| 0.616 | 23100 | 0.015 | 0.0126 | 0.9754 |
| 0.6187 | 23200 | 0.0128 | 0.0124 | 0.9759 |
| 0.6213 | 23300 | 0.0127 | 0.0126 | 0.9758 |
| 0.624 | 23400 | 0.0137 | 0.0126 | 0.9755 |
| 0.6267 | 23500 | 0.0171 | 0.0125 | 0.9760 |
| 0.6293 | 23600 | 0.0154 | 0.0123 | 0.9761 |
| 0.632 | 23700 | 0.0133 | 0.0125 | 0.9757 |
| 0.6347 | 23800 | 0.0147 | 0.0122 | 0.9762 |
| 0.6373 | 23900 | 0.012 | 0.0123 | 0.9759 |
| 0.64 | 24000 | 0.0121 | 0.0124 | 0.9762 |
| 0.6427 | 24100 | 0.0156 | 0.0122 | 0.9768 |
| 0.6453 | 24200 | 0.0135 | 0.0122 | 0.9763 |
| 0.648 | 24300 | 0.0111 | 0.0123 | 0.9762 |
| 0.6507 | 24400 | 0.0131 | 0.0121 | 0.9766 |
| 0.6533 | 24500 | 0.0166 | 0.0120 | 0.9766 |
| 0.656 | 24600 | 0.0145 | 0.0121 | 0.9764 |
| 0.6587 | 24700 | 0.0138 | 0.0122 | 0.9763 |
| 0.6613 | 24800 | 0.0127 | 0.0120 | 0.9766 |
| 0.664 | 24900 | 0.0142 | 0.0120 | 0.9767 |
| 0.6667 | 25000 | 0.0119 | 0.0122 | 0.9764 |
| 0.6693 | 25100 | 0.0157 | 0.0120 | 0.9768 |
| 0.672 | 25200 | 0.0126 | 0.0119 | 0.9769 |
| 0.6747 | 25300 | 0.0113 | 0.0119 | 0.9772 |
| 0.6773 | 25400 | 0.0138 | 0.0121 | 0.9767 |
| 0.68 | 25500 | 0.0135 | 0.0124 | 0.9759 |
| 0.6827 | 25600 | 0.0147 | 0.0120 | 0.9765 |
| 0.6853 | 25700 | 0.0119 | 0.0120 | 0.9764 |
| 0.688 | 25800 | 0.0167 | 0.0120 | 0.9765 |
| 0.6907 | 25900 | 0.0132 | 0.0120 | 0.9767 |
| 0.6933 | 26000 | 0.0144 | 0.0118 | 0.9768 |
| 0.696 | 26100 | 0.0135 | 0.0118 | 0.9771 |
| 0.6987 | 26200 | 0.0156 | 0.0119 | 0.9769 |
| 0.7013 | 26300 | 0.0132 | 0.0119 | 0.9769 |
| 0.704 | 26400 | 0.0139 | 0.0120 | 0.9769 |
| 0.7067 | 26500 | 0.014 | 0.0118 | 0.9771 |
| 0.7093 | 26600 | 0.0133 | 0.0118 | 0.9770 |
| 0.712 | 26700 | 0.0142 | 0.0118 | 0.9773 |
| 0.7147 | 26800 | 0.0113 | 0.0117 | 0.977 |
| 0.7173 | 26900 | 0.0142 | 0.0117 | 0.977 |
| 0.72 | 27000 | 0.0112 | 0.0117 | 0.9771 |
| 0.7227 | 27100 | 0.012 | 0.0118 | 0.9768 |
| 0.7253 | 27200 | 0.0135 | 0.0117 | 0.9768 |
| 0.728 | 27300 | 0.0126 | 0.0116 | 0.9769 |
| 0.7307 | 27400 | 0.0136 | 0.0117 | 0.9767 |
| 0.7333 | 27500 | 0.013 | 0.0116 | 0.9770 |
| 0.736 | 27600 | 0.0131 | 0.0117 | 0.9767 |
| 0.7387 | 27700 | 0.0127 | 0.0116 | 0.9772 |
| 0.7413 | 27800 | 0.0124 | 0.0116 | 0.9770 |
| 0.744 | 27900 | 0.011 | 0.0116 | 0.9771 |
| 0.7467 | 28000 | 0.0159 | 0.0116 | 0.9770 |
| 0.7493 | 28100 | 0.0118 | 0.0116 | 0.9770 |
| 0.752 | 28200 | 0.0146 | 0.0115 | 0.9773 |
| 0.7547 | 28300 | 0.0112 | 0.0116 | 0.9772 |
| 0.7573 | 28400 | 0.0116 | 0.0115 | 0.9776 |
| 0.76 | 28500 | 0.0115 | 0.0115 | 0.9775 |
| 0.7627 | 28600 | 0.0137 | 0.0115 | 0.9779 |
| 0.7653 | 28700 | 0.0106 | 0.0115 | 0.9777 |
| 0.768 | 28800 | 0.011 | 0.0116 | 0.9774 |
| 0.7707 | 28900 | 0.0132 | 0.0115 | 0.9774 |
| 0.7733 | 29000 | 0.0119 | 0.0114 | 0.9776 |
| 0.776 | 29100 | 0.0121 | 0.0114 | 0.9779 |
| 0.7787 | 29200 | 0.0136 | 0.0113 | 0.9780 |
| 0.7813 | 29300 | 0.0114 | 0.0114 | 0.9779 |
| 0.784 | 29400 | 0.0122 | 0.0115 | 0.9778 |
| 0.7867 | 29500 | 0.0117 | 0.0114 | 0.9780 |
| 0.7893 | 29600 | 0.0119 | 0.0114 | 0.9778 |
| 0.792 | 29700 | 0.0145 | 0.0114 | 0.9778 |
| 0.7947 | 29800 | 0.0098 | 0.0113 | 0.9779 |
| 0.7973 | 29900 | 0.015 | 0.0114 | 0.9777 |
| 0.8 | 30000 | 0.0123 | 0.0113 | 0.9779 |
| 0.8027 | 30100 | 0.0111 | 0.0115 | 0.9774 |
| 0.8053 | 30200 | 0.0126 | 0.0114 | 0.9778 |
| 0.808 | 30300 | 0.0131 | 0.0113 | 0.9783 |
| 0.8107 | 30400 | 0.0131 | 0.0113 | 0.9784 |
| 0.8133 | 30500 | 0.0113 | 0.0113 | 0.9783 |
| 0.816 | 30600 | 0.0131 | 0.0113 | 0.9783 |
| 0.8187 | 30700 | 0.0137 | 0.0113 | 0.9782 |
| 0.8213 | 30800 | 0.0119 | 0.0112 | 0.9784 |
| 0.824 | 30900 | 0.0127 | 0.0113 | 0.9782 |
| 0.8267 | 31000 | 0.0114 | 0.0112 | 0.9787 |
| 0.8293 | 31100 | 0.0116 | 0.0111 | 0.9784 |
| 0.832 | 31200 | 0.0117 | 0.0112 | 0.9784 |
| 0.8347 | 31300 | 0.0128 | 0.0112 | 0.9782 |
| 0.8373 | 31400 | 0.0125 | 0.0112 | 0.9782 |
| 0.84 | 31500 | 0.0136 | 0.0111 | 0.9787 |
| 0.8427 | 31600 | 0.0121 | 0.0111 | 0.9785 |
| 0.8453 | 31700 | 0.0137 | 0.0112 | 0.9785 |
| 0.848 | 31800 | 0.0115 | 0.0111 | 0.9786 |
| 0.8507 | 31900 | 0.0111 | 0.0111 | 0.9784 |
| 0.8533 | 32000 | 0.012 | 0.0111 | 0.9786 |
| 0.856 | 32100 | 0.0115 | 0.0111 | 0.9787 |
| 0.8587 | 32200 | 0.0125 | 0.0111 | 0.9785 |
| 0.8613 | 32300 | 0.0111 | 0.0111 | 0.9788 |
| 0.864 | 32400 | 0.0127 | 0.0111 | 0.9788 |
| 0.8667 | 32500 | 0.0126 | 0.0110 | 0.9788 |
| 0.8693 | 32600 | 0.012 | 0.0111 | 0.9788 |
| 0.872 | 32700 | 0.0117 | 0.0111 | 0.9787 |
| 0.8747 | 32800 | 0.0136 | 0.0110 | 0.9787 |
| 0.8773 | 32900 | 0.0118 | 0.0110 | 0.9788 |
| 0.88 | 33000 | 0.015 | 0.0110 | 0.9789 |
| 0.8827 | 33100 | 0.0105 | 0.0110 | 0.9788 |
| 0.8853 | 33200 | 0.0135 | 0.0110 | 0.9786 |
| 0.888 | 33300 | 0.0099 | 0.0110 | 0.9790 |
| 0.8907 | 33400 | 0.013 | 0.0109 | 0.9787 |
| 0.8933 | 33500 | 0.0149 | 0.0109 | 0.9788 |
| 0.896 | 33600 | 0.012 | 0.0109 | 0.9789 |
| 0.8987 | 33700 | 0.01 | 0.0110 | 0.9788 |
| 0.9013 | 33800 | 0.0132 | 0.0110 | 0.9788 |
| 0.904 | 33900 | 0.0138 | 0.0109 | 0.9791 |
| 0.9067 | 34000 | 0.0107 | 0.0109 | 0.9789 |
| 0.9093 | 34100 | 0.0133 | 0.0109 | 0.9789 |
| 0.912 | 34200 | 0.0124 | 0.0109 | 0.9788 |
| 0.9147 | 34300 | 0.0119 | 0.0109 | 0.9788 |
| 0.9173 | 34400 | 0.0101 | 0.0109 | 0.9787 |
| 0.92 | 34500 | 0.0135 | 0.0109 | 0.9790 |
| 0.9227 | 34600 | 0.0116 | 0.0109 | 0.9789 |
| 0.9253 | 34700 | 0.0116 | 0.0109 | 0.9791 |
| 0.928 | 34800 | 0.0082 | 0.0108 | 0.9791 |
| 0.9307 | 34900 | 0.0129 | 0.0108 | 0.9791 |
| 0.9333 | 35000 | 0.0129 | 0.0108 | 0.9792 |
| 0.936 | 35100 | 0.0147 | 0.0108 | 0.9791 |
| 0.9387 | 35200 | 0.0112 | 0.0108 | 0.9790 |
| 0.9413 | 35300 | 0.0108 | 0.0108 | 0.9790 |
| 0.944 | 35400 | 0.0114 | 0.0108 | 0.9791 |
| 0.9467 | 35500 | 0.0096 | 0.0108 | 0.9792 |
| 0.9493 | 35600 | 0.0111 | 0.0108 | 0.9790 |
| 0.952 | 35700 | 0.0131 | 0.0108 | 0.9790 |
| 0.9547 | 35800 | 0.0147 | 0.0108 | 0.9792 |
| 0.9573 | 35900 | 0.0121 | 0.0108 | 0.9792 |
| 0.96 | 36000 | 0.0105 | 0.0108 | 0.9791 |
| 0.9627 | 36100 | 0.0081 | 0.0108 | 0.9791 |
| 0.9653 | 36200 | 0.013 | 0.0108 | 0.9791 |
| 0.968 | 36300 | 0.0121 | 0.0108 | 0.9792 |
| 0.9707 | 36400 | 0.0122 | 0.0108 | 0.9792 |
| 0.9733 | 36500 | 0.0121 | 0.0108 | 0.9792 |
| 0.976 | 36600 | 0.011 | 0.0108 | 0.9792 |
| 0.9787 | 36700 | 0.0109 | 0.0107 | 0.9792 |
| 0.9813 | 36800 | 0.0114 | 0.0107 | 0.9792 |
| 0.984 | 36900 | 0.0113 | 0.0107 | 0.9793 |
| 0.9867 | 37000 | 0.0111 | 0.0107 | 0.9794 |
| 0.9893 | 37100 | 0.0097 | 0.0107 | 0.9793 |
| 0.992 | 37200 | 0.0127 | 0.0107 | 0.9793 |
| 0.9947 | 37300 | 0.0143 | 0.0107 | 0.9794 |
| 0.9973 | 37400 | 0.0103 | 0.0107 | 0.9794 |
| 1.0 | 37500 | 0.014 | 0.0107 | 0.9780 |
</details>
### Framework Versions
- Python: 3.11.1
- Sentence Transformers: 3.3.1
- Transformers: 4.47.0
- PyTorch: 2.1.1+cu121
- Accelerate: 1.2.0
- Datasets: 2.18.0
- 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",
}
```
#### TripletLoss
```bibtex
@misc{hermans2017defense,
title={In Defense of the Triplet Loss for Person Re-Identification},
author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
year={2017},
eprint={1703.07737},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
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