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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:360886
- loss:CoSENTLoss
base_model: sentence-transformers/all-mpnet-base-v2
widget:
- source_sentence: '|Immunosuppressant drug therapy (procedure)| : { |Method (attribute)|
= |Administration - action (qualifier value)|, |Direct substance (attribute)|
= |Auranofin (substance)| }, { |Has intent (attribute)| = |Therapeutic intent
(qualifier value)| }'
sentences:
- Tofacitinib therapy (procedure)
- Mural thrombus of right ventricle following acute myocardial infarction (disorder)
- Neonatal botulism (disorder)
- source_sentence: '|Injury of finger of left hand (disorder)| + |Traumatic blister
of index finger (disorder)| + |Traumatic blister of left hand (disorder)| : {
|Finding site (attribute)| = |Skin structure of left index finger (body structure)|,
|Associated morphology (attribute)| = |Blister (morphologic abnormality)| }, {
|Due to (attribute)| = |Traumatic event (event)| }'
sentences:
- Cardiovascular system closure (procedure)
- Entire skin of lower eyelid and periocular area (body structure)
- Avulsion of nail unit of left little finger (disorder)
- source_sentence: '|Evaluation finding (finding)| : { |Interprets (attribute)| =
|Interferon gamma assay (procedure)|, |Has interpretation (attribute)| = |Positive
(qualifier value)| }'
sentences:
- Gleason pattern (observable entity)
- Interferon gamma assay positive (finding)
- Intentional melphalan overdose (disorder)
- source_sentence: '|Finding of specific antibody level (finding)| : { |Interprets
(attribute)| = |Measurement of viral antibody (procedure)| }'
sentences:
- Lyme detected by immunoblot (finding)
- Primary malignant neoplasm of accessory sinus (disorder)
- Perfusion of lymphatics with hyperthermia (procedure)
- source_sentence: '|Neoplasm of anterior wall of nasopharynx (disorder)| + |Neoplasm
of uncertain behavior of nasopharynx (disorder)| : { |Finding site (attribute)|
= |Structure of anterior wall of nasopharynx (body structure)|, |Associated morphology
(attribute)| = |Neoplasm of uncertain behavior (morphologic abnormality)| }'
sentences:
- Secondary angle-closure glaucoma - synechial (disorder)
- Neoplasm of uncertain behavior of lateral wall of nasopharynx (disorder)
- Product containing precisely cefamandole (as cefamandole nafate) 1 gram/1 vial
powder for conventional release solution for injection (clinical drug)
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts-dev
metrics:
- type: pearson_cosine
value: 0.9048593944190657
name: Pearson Cosine
- type: spearman_cosine
value: 0.8556279874385214
name: Spearman Cosine
---
# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) on the csv 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 9a3225965996d404b775526de6dbfe85d3368642 -->
- **Maximum Sequence Length:** 384 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- csv
<!-- - **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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(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})
(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/all-mpnet-base-v2_snomed_expression")
# Run inference
sentences = [
'|Neoplasm of anterior wall of nasopharynx (disorder)| + |Neoplasm of uncertain behavior of nasopharynx (disorder)| : { |Finding site (attribute)| = |Structure of anterior wall of nasopharynx (body structure)|, |Associated morphology (attribute)| = |Neoplasm of uncertain behavior (morphologic abnormality)| }',
'Neoplasm of uncertain behavior of lateral wall of nasopharynx (disorder)',
'Secondary angle-closure glaucoma - synechial (disorder)',
]
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 | 0.9049 |
| **spearman_cosine** | **0.8556** |
<!--
## 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
#### csv
* Dataset: csv
* Size: 360,886 training samples
* Columns: <code>text_a</code>, <code>text_b</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | text_a | text_b | label |
|:--------|:-------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 28 tokens</li><li>mean: 101.13 tokens</li><li>max: 357 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.29 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>0: ~51.40%</li><li>1: ~48.60%</li></ul> |
* Samples:
| text_a | text_b | label |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------|:---------------|
| <code>|Risk assessment (procedure)| : { |Method (attribute)| = |Evaluation - action (qualifier value)| }, { |Has focus (attribute)| = |At increased risk of ineffective tissue perfusion (finding)| }</code> | <code>Assessment of risk of ineffective tissue perfusion (procedure)</code> | <code>1</code> |
| <code>|Chronic inflammatory disorder (disorder)| + |Chronic nervous system disorder (disorder)| + |Meningitis (disorder)| : { |Finding site (attribute)| = |Meninges structure (body structure)|, |Associated morphology (attribute)| = |Chronic inflammatory morphology (morphologic abnormality)| }, { |Clinical course (attribute)| = |Chronic (qualifier value)| }</code> | <code>Chronic meningitis (disorder)</code> | <code>1</code> |
| <code>|Imaging of head (procedure)| + |Ultrasound procedure on topographic region (procedure)| : { |Method (attribute)| = |Ultrasound imaging - action (qualifier value)|, |Procedure site - Direct (attribute)| = |Head structure (body structure)| }</code> | <code>Imaging of brain (procedure)</code> | <code>0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Evaluation Dataset
#### csv
* Dataset: csv
* Size: 360,886 evaluation samples
* Columns: <code>text_a</code>, <code>text_b</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | text_a | text_b | label |
|:--------|:-------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 25 tokens</li><li>mean: 101.18 tokens</li><li>max: 366 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.21 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>0: ~51.30%</li><li>1: ~48.70%</li></ul> |
* Samples:
| text_a | text_b | label |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------|
| <code>|Disorder of fetal abdominal region (disorder)| + |Fetal genitourinary abnormality (disorder)| + |Kidney disease (disorder)| : { |Occurrence (attribute)| = |Fetal period (qualifier value)|, |Finding site (attribute)| = |Kidney structure (body structure)|, |Associated morphology (attribute)| = |Morphologically abnormal structure (morphologic abnormality)|, |Pathological process (attribute)| = |Pathological developmental process (qualifier value)| }</code> | <code>Early urethral obstruction sequence (disorder)</code> | <code>0</code> |
| <code>|Computed tomography of pelvis for brachytherapy planning (procedure)| + |Computed tomography of prostate for radiotherapy planning (procedure)| : { |Has focus (attribute)| = |Treatment planning for brachytherapy (procedure)| }, { |Method (attribute)| = |Computed tomography imaging - action (qualifier value)|, |Procedure site - Direct (attribute)| = |Prostatic structure (body structure)| }</code> | <code>Computed tomography of prostate with contrast for radiotherapy planning (procedure)</code> | <code>0</code> |
| <code>|Product containing only hydroxyzine in oral dose form (medicinal product form)| : |Has manufactured dose form (attribute)| = |Conventional release oral capsule (dose form)|, |Has unit of presentation (attribute)| = |Capsule (unit of presentation)|, |Count of base of active ingredient (attribute)| = #1, { |Has precise active ingredient (attribute)| = |Hydroxyzine pamoate (substance)|, |Has basis of strength substance (attribute)| = |Hydroxyzine pamoate (substance)|, |Has presentation strength numerator value (attribute)| = #100, |Has presentation strength numerator unit (attribute)| = |milligram (qualifier value)|, |Has presentation strength denominator value (attribute)| = #1, |Has presentation strength denominator unit (attribute)| = |Capsule (unit of presentation)| }</code> | <code>Hydroxyzine pamoate 100mg capsule (product)</code> | <code>1</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### 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 | sts-dev_spearman_cosine |
|:------:|:-----:|:-------------:|:---------------:|:-----------------------:|
| 0.0055 | 100 | 5.2922 | 3.9427 | 0.6159 |
| 0.0111 | 200 | 3.2766 | 2.8638 | 0.7437 |
| 0.0166 | 300 | 2.8445 | 2.4816 | 0.7833 |
| 0.0222 | 400 | 2.5209 | 2.2995 | 0.7974 |
| 0.0277 | 500 | 2.5298 | 2.1033 | 0.8072 |
| 0.0333 | 600 | 2.0427 | 2.1055 | 0.8114 |
| 0.0388 | 700 | 2.1367 | 2.0634 | 0.8121 |
| 0.0443 | 800 | 2.2486 | 1.7848 | 0.8210 |
| 0.0499 | 900 | 1.921 | 1.9666 | 0.8190 |
| 0.0554 | 1000 | 1.9962 | 1.9688 | 0.8180 |
| 0.0610 | 1100 | 1.5203 | 2.0695 | 0.8187 |
| 0.0665 | 1200 | 2.0616 | 1.7060 | 0.8223 |
| 0.0720 | 1300 | 2.0793 | 1.8158 | 0.8254 |
| 0.0776 | 1400 | 2.0766 | 1.8549 | 0.8213 |
| 0.0831 | 1500 | 1.5608 | 1.8045 | 0.8241 |
| 0.0887 | 1600 | 1.7671 | 1.9724 | 0.8196 |
| 0.0942 | 1700 | 2.1665 | 2.2623 | 0.8033 |
| 0.0998 | 1800 | 1.9596 | 1.8070 | 0.8224 |
| 0.1053 | 1900 | 1.5704 | 1.8142 | 0.8265 |
| 0.1108 | 2000 | 2.0749 | 2.0596 | 0.8205 |
| 0.1164 | 2100 | 1.9445 | 1.7458 | 0.8279 |
| 0.1219 | 2200 | 1.6043 | 2.0309 | 0.8242 |
| 0.1275 | 2300 | 1.5723 | 1.7440 | 0.8286 |
| 0.1330 | 2400 | 1.7905 | 1.5584 | 0.8319 |
| 0.1385 | 2500 | 2.0777 | 1.7437 | 0.8254 |
| 0.1441 | 2600 | 1.7563 | 1.6852 | 0.8322 |
| 0.1496 | 2700 | 1.6565 | 1.8196 | 0.8268 |
| 0.1552 | 2800 | 1.5064 | 1.6763 | 0.8302 |
| 0.1607 | 2900 | 1.9221 | 1.7317 | 0.8279 |
| 0.1663 | 3000 | 1.7803 | 1.8330 | 0.8225 |
| 0.1718 | 3100 | 1.3559 | 1.9419 | 0.8278 |
| 0.1773 | 3200 | 1.5309 | 1.5263 | 0.8345 |
| 0.1829 | 3300 | 1.6429 | 1.7952 | 0.8290 |
| 0.1884 | 3400 | 1.4676 | 1.8284 | 0.8270 |
| 0.1940 | 3500 | 1.5167 | 1.6084 | 0.8295 |
| 0.1995 | 3600 | 1.7605 | 1.6362 | 0.8334 |
| 0.2050 | 3700 | 1.6812 | 1.4205 | 0.8348 |
| 0.2106 | 3800 | 1.4537 | 1.6432 | 0.8341 |
| 0.2161 | 3900 | 1.6718 | 1.2594 | 0.8382 |
| 0.2217 | 4000 | 1.3892 | 1.4798 | 0.8351 |
| 0.2272 | 4100 | 1.7261 | 1.3948 | 0.8354 |
| 0.2328 | 4200 | 1.6611 | 1.4519 | 0.8368 |
| 0.2383 | 4300 | 1.3181 | 1.2844 | 0.8389 |
| 0.2438 | 4400 | 1.4356 | 1.3015 | 0.8392 |
| 0.2494 | 4500 | 1.4077 | 1.3217 | 0.8381 |
| 0.2549 | 4600 | 1.2534 | 1.5767 | 0.8340 |
| 0.2605 | 4700 | 1.6881 | 1.2737 | 0.8398 |
| 0.2660 | 4800 | 1.4572 | 1.2570 | 0.8408 |
| 0.2715 | 4900 | 1.2339 | 1.1919 | 0.8423 |
| 0.2771 | 5000 | 1.2871 | 1.3166 | 0.8398 |
| 0.2826 | 5100 | 1.3532 | 1.4045 | 0.8360 |
| 0.2882 | 5200 | 1.2731 | 1.4843 | 0.8384 |
| 0.2937 | 5300 | 1.3776 | 1.1347 | 0.8423 |
| 0.2993 | 5400 | 1.2179 | 1.5040 | 0.8373 |
| 0.3048 | 5500 | 1.41 | 1.2401 | 0.8418 |
| 0.3103 | 5600 | 1.3901 | 1.1494 | 0.8416 |
| 0.3159 | 5700 | 1.4007 | 1.2487 | 0.8414 |
| 0.3214 | 5800 | 1.3444 | 1.4062 | 0.8397 |
| 0.3270 | 5900 | 1.3671 | 1.3194 | 0.8410 |
| 0.3325 | 6000 | 1.2401 | 1.2642 | 0.8411 |
| 0.3380 | 6100 | 1.4102 | 1.3317 | 0.8392 |
| 0.3436 | 6200 | 1.1672 | 1.0846 | 0.8438 |
| 0.3491 | 6300 | 1.3595 | 1.2747 | 0.8387 |
| 0.3547 | 6400 | 1.0956 | 1.4071 | 0.8392 |
| 0.3602 | 6500 | 1.539 | 1.2683 | 0.8413 |
| 0.3658 | 6600 | 1.3078 | 1.2173 | 0.8430 |
| 0.3713 | 6700 | 1.3562 | 1.0733 | 0.8447 |
| 0.3768 | 6800 | 1.3009 | 1.3561 | 0.8408 |
| 0.3824 | 6900 | 1.4319 | 1.1958 | 0.8432 |
| 0.3879 | 7000 | 1.0702 | 1.1325 | 0.8437 |
| 0.3935 | 7100 | 1.2339 | 0.9852 | 0.8465 |
| 0.3990 | 7200 | 0.8772 | 1.2658 | 0.8419 |
| 0.4045 | 7300 | 1.3411 | 1.1585 | 0.8438 |
| 0.4101 | 7400 | 1.1518 | 1.1572 | 0.8439 |
| 0.4156 | 7500 | 1.0287 | 0.9960 | 0.8456 |
| 0.4212 | 7600 | 1.2913 | 1.1595 | 0.8437 |
| 0.4267 | 7700 | 1.1006 | 1.1575 | 0.8437 |
| 0.4323 | 7800 | 1.3463 | 1.0478 | 0.8459 |
| 0.4378 | 7900 | 1.0428 | 1.0495 | 0.8461 |
| 0.4433 | 8000 | 1.0657 | 1.0442 | 0.8465 |
| 0.4489 | 8100 | 1.1002 | 1.0223 | 0.8475 |
| 0.4544 | 8200 | 1.1596 | 1.0066 | 0.8474 |
| 0.4600 | 8300 | 1.3218 | 1.0403 | 0.8460 |
| 0.4655 | 8400 | 1.1482 | 1.1177 | 0.8457 |
| 0.4710 | 8500 | 1.0033 | 1.1743 | 0.8448 |
| 0.4766 | 8600 | 1.0772 | 1.1071 | 0.8464 |
| 0.4821 | 8700 | 0.775 | 1.2731 | 0.8438 |
| 0.4877 | 8800 | 0.8859 | 0.9293 | 0.8491 |
| 0.4932 | 8900 | 0.7837 | 1.0760 | 0.8462 |
| 0.4988 | 9000 | 0.7768 | 1.0135 | 0.8470 |
| 0.5043 | 9100 | 1.0103 | 0.9691 | 0.8477 |
| 0.5098 | 9200 | 1.0219 | 1.2059 | 0.8441 |
| 0.5154 | 9300 | 0.9093 | 1.0895 | 0.8461 |
| 0.5209 | 9400 | 1.0176 | 0.9229 | 0.8489 |
| 0.5265 | 9500 | 1.3811 | 0.9470 | 0.8483 |
| 0.5320 | 9600 | 0.8338 | 1.0048 | 0.8477 |
| 0.5375 | 9700 | 0.7105 | 1.0591 | 0.8464 |
| 0.5431 | 9800 | 1.0313 | 0.9789 | 0.8482 |
| 0.5486 | 9900 | 1.0308 | 0.8741 | 0.8499 |
| 0.5542 | 10000 | 0.7353 | 0.9419 | 0.8482 |
| 0.5597 | 10100 | 0.7683 | 1.0695 | 0.8473 |
| 0.5653 | 10200 | 1.1728 | 0.9705 | 0.8494 |
| 0.5708 | 10300 | 0.8578 | 0.9633 | 0.8493 |
| 0.5763 | 10400 | 1.0095 | 0.7799 | 0.8514 |
| 0.5819 | 10500 | 1.0157 | 1.0333 | 0.8485 |
| 0.5874 | 10600 | 0.8164 | 0.8596 | 0.8509 |
| 0.5930 | 10700 | 0.9278 | 0.8256 | 0.8516 |
| 0.5985 | 10800 | 0.5919 | 1.0104 | 0.8493 |
| 0.6040 | 10900 | 0.6931 | 0.9957 | 0.8492 |
| 0.6096 | 11000 | 1.1545 | 0.9758 | 0.8494 |
| 0.6151 | 11100 | 1.1061 | 1.0360 | 0.8493 |
| 0.6207 | 11200 | 0.7954 | 0.9362 | 0.8509 |
| 0.6262 | 11300 | 0.6365 | 0.9504 | 0.8511 |
| 0.6318 | 11400 | 0.992 | 0.8553 | 0.8521 |
| 0.6373 | 11500 | 0.6971 | 0.8763 | 0.8520 |
| 0.6428 | 11600 | 0.8162 | 0.9527 | 0.8504 |
| 0.6484 | 11700 | 0.8973 | 0.8722 | 0.8519 |
| 0.6539 | 11800 | 0.7652 | 0.9417 | 0.8510 |
| 0.6595 | 11900 | 0.7305 | 0.8955 | 0.8519 |
| 0.6650 | 12000 | 0.8555 | 0.9007 | 0.8510 |
| 0.6705 | 12100 | 0.7165 | 0.7924 | 0.8530 |
| 0.6761 | 12200 | 0.7939 | 0.8607 | 0.8516 |
| 0.6816 | 12300 | 0.9873 | 0.7780 | 0.8533 |
| 0.6872 | 12400 | 0.7197 | 0.9380 | 0.8508 |
| 0.6927 | 12500 | 1.076 | 0.8041 | 0.8531 |
| 0.6983 | 12600 | 0.6853 | 0.8800 | 0.8517 |
| 0.7038 | 12700 | 0.9403 | 0.8181 | 0.8527 |
| 0.7093 | 12800 | 0.8598 | 0.7641 | 0.8536 |
| 0.7149 | 12900 | 0.628 | 0.7479 | 0.8540 |
| 0.7204 | 13000 | 1.0517 | 0.7611 | 0.8536 |
| 0.7260 | 13100 | 0.5099 | 0.8426 | 0.8521 |
| 0.7315 | 13200 | 0.751 | 0.8133 | 0.8526 |
| 0.7370 | 13300 | 0.572 | 0.8344 | 0.8524 |
| 0.7426 | 13400 | 0.8213 | 0.7869 | 0.8528 |
| 0.7481 | 13500 | 0.6046 | 0.7810 | 0.8528 |
| 0.7537 | 13600 | 0.7211 | 0.7502 | 0.8537 |
| 0.7592 | 13700 | 0.7443 | 0.7398 | 0.8538 |
| 0.7648 | 13800 | 0.6644 | 0.8257 | 0.8529 |
| 0.7703 | 13900 | 0.8948 | 0.7271 | 0.8536 |
| 0.7758 | 14000 | 0.6886 | 0.7607 | 0.8531 |
| 0.7814 | 14100 | 0.8322 | 0.7143 | 0.8540 |
| 0.7869 | 14200 | 0.6965 | 0.7270 | 0.8540 |
| 0.7925 | 14300 | 0.6478 | 0.7368 | 0.8541 |
| 0.7980 | 14400 | 0.6877 | 0.7690 | 0.8532 |
| 0.8035 | 14500 | 0.6289 | 0.7316 | 0.8538 |
| 0.8091 | 14600 | 0.9058 | 0.6514 | 0.8548 |
| 0.8146 | 14700 | 0.5971 | 0.6980 | 0.8542 |
| 0.8202 | 14800 | 0.5774 | 0.7124 | 0.8539 |
| 0.8257 | 14900 | 0.6134 | 0.7480 | 0.8534 |
| 0.8313 | 15000 | 0.6962 | 0.6284 | 0.8551 |
| 0.8368 | 15100 | 0.5934 | 0.7099 | 0.8540 |
| 0.8423 | 15200 | 0.7791 | 0.6925 | 0.8542 |
| 0.8479 | 15300 | 0.5418 | 0.6774 | 0.8544 |
| 0.8534 | 15400 | 0.7526 | 0.6380 | 0.8552 |
| 0.8590 | 15500 | 0.694 | 0.6967 | 0.8543 |
| 0.8645 | 15600 | 0.5813 | 0.6864 | 0.8543 |
| 0.8700 | 15700 | 0.726 | 0.6325 | 0.8552 |
| 0.8756 | 15800 | 0.5094 | 0.6491 | 0.8549 |
| 0.8811 | 15900 | 0.5728 | 0.6549 | 0.8549 |
| 0.8867 | 16000 | 0.5272 | 0.6723 | 0.8548 |
| 0.8922 | 16100 | 0.6896 | 0.6786 | 0.8546 |
| 0.8978 | 16200 | 0.5666 | 0.6629 | 0.8550 |
| 0.9033 | 16300 | 0.7312 | 0.6801 | 0.8549 |
| 0.9088 | 16400 | 0.6451 | 0.6779 | 0.8549 |
| 0.9144 | 16500 | 0.6572 | 0.6374 | 0.8556 |
| 0.9199 | 16600 | 0.5052 | 0.6672 | 0.8551 |
| 0.9255 | 16700 | 0.5395 | 0.6686 | 0.8550 |
| 0.9310 | 16800 | 0.4715 | 0.6840 | 0.8547 |
| 0.9365 | 16900 | 0.7149 | 0.6576 | 0.8552 |
| 0.9421 | 17000 | 0.5066 | 0.6533 | 0.8553 |
| 0.9476 | 17100 | 0.6382 | 0.6509 | 0.8552 |
| 0.9532 | 17200 | 0.5585 | 0.6729 | 0.8550 |
| 0.9587 | 17300 | 0.5953 | 0.6505 | 0.8554 |
| 0.9643 | 17400 | 0.3545 | 0.6487 | 0.8555 |
| 0.9698 | 17500 | 0.8031 | 0.6451 | 0.8555 |
| 0.9753 | 17600 | 0.8531 | 0.6366 | 0.8557 |
| 0.9809 | 17700 | 0.7154 | 0.6365 | 0.8557 |
| 0.9864 | 17800 | 0.3339 | 0.6339 | 0.8557 |
| 0.9920 | 17900 | 0.5858 | 0.6410 | 0.8556 |
| 0.9975 | 18000 | 0.7509 | 0.6400 | 0.8556 |
</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",
}
```
#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
author={Su Jianlin},
year={2022},
month={Jan},
url={https://kexue.fm/archives/8847},
}
```
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