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---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:1M<n<10M
- loss:CoSENTLoss
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
base_model: distilbert/distilbert-base-uncased
widget:
- source_sentence: B C C_L CENTER TUNNEL VERT Other XXXX GENERIC G-S
sentences:
- T L ENG TO RAD SWITCH 90 Deg Front 2015 P552 VOLTS
- T RCM ENS 071 RCM ENS EFPR VOLT 90 Deg Front 2021 CX430 VOLTS
- T L ROCKER AT B PILLAR LONG 90 Deg Front 2020 V363N G-S
- source_sentence: T L F DUMMY PELVIS LAT 90 Deg Front 2021 P702 G-S
sentences:
- T L F DUMMY PELVIS LAT 90 Deg Front 2021 CX727 G-S
- T FIXTURE BASE FRONT ACCEL VERT ACCEL Linear Test 2025 U717 G-S
- T R ROCKER AT B_PILLAR LONG 30 Deg Front Angular Right 2025 CX430 G-S
- source_sentence: T L F DUMMY PELVIS LAT 90 Deg Front 2021 CX727 G-S
sentences:
- T R F DUMMY PELVIS LAT 90 Deg Front 2021 P702 G-S
- T L F DUMMY PELVIS LONG 30 Deg Front Angular Left 2020 P558 G-S
- T R F DUMMY L LOWER TIBIA MY LOAD 90 Deg Front 2022 U553 IN-LBS
- source_sentence: T R F DUMMY CHEST VERT 90 Deg Front 2021 P702 G-S
sentences:
- T R F DUMMY CHEST VERT 90 Deg Front 2015 P552 G-S
- T L F DUMMY R LOWER TIBIA MX LOAD 90 Deg Front 2021 CX727 IN-LBS
- T REAR DIFFERENTIAL LONG 30 Deg Front Angular Left 2020 P558 G-S
- source_sentence: T ENGINE TRANS TOP LAT 90 Deg Front 2025 U717 G-S
sentences:
- T R F ACTIVE VENT SQUIB VOLT 90 Deg Front 2021 P702 VOLTS
- T ENGINE TRANS TOP LAT 30 Deg Front Angular Left 2020 P558 G-S
- T R F DUMMY CHEST VERT 90 Deg Frontal Impact Simulation 2024 CX727 G-S
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on distilbert/distilbert-base-uncased
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts-dev
metrics:
- type: pearson_cosine
value: 0.4517523751963131
name: Pearson Cosine
- type: spearman_cosine
value: 0.4761555869182568
name: Spearman Cosine
- type: pearson_manhattan
value: 0.42531457338882206
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.46381946353811704
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.4261708588640235
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.4651666003446995
name: Spearman Euclidean
- type: pearson_dot
value: 0.3897944292190218
name: Pearson Dot
- type: spearman_dot
value: 0.37404050621023377
name: Spearman Dot
- type: pearson_max
value: 0.4517523751963131
name: Pearson Max
- type: spearman_max
value: 0.4761555869182568
name: Spearman Max
- type: pearson_cosine
value: 0.4412143708585779
name: Pearson Cosine
- type: spearman_cosine
value: 0.4670631031564122
name: Spearman Cosine
- type: pearson_manhattan
value: 0.4156386809751022
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.4559676784726118
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.41671687323124873
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.45746069501329756
name: Spearman Euclidean
- type: pearson_dot
value: 0.37528926047569405
name: Pearson Dot
- type: spearman_dot
value: 0.36286227520562186
name: Spearman Dot
- type: pearson_max
value: 0.4412143708585779
name: Pearson Max
- type: spearman_max
value: 0.4670631031564122
name: Spearman Max
---
# SentenceTransformer based on distilbert/distilbert-base-uncased
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased). 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:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) <!-- at revision 12040accade4e8a0f71eabdb258fecc2e7e948be -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **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': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
(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("sentence_transformers_model_id")
# Run inference
sentences = [
'T ENGINE TRANS TOP LAT 90 Deg Front 2025 U717 G-S',
'T R F ACTIVE VENT SQUIB VOLT 90 Deg Front 2021 P702 VOLTS',
'T ENGINE TRANS TOP LAT 30 Deg Front Angular Left 2020 P558 G-S',
]
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 | 0.4518 |
| **spearman_cosine** | **0.4762** |
| pearson_manhattan | 0.4253 |
| spearman_manhattan | 0.4638 |
| pearson_euclidean | 0.4262 |
| spearman_euclidean | 0.4652 |
| pearson_dot | 0.3898 |
| spearman_dot | 0.374 |
| pearson_max | 0.4518 |
| spearman_max | 0.4762 |
#### 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 | 0.4412 |
| **spearman_cosine** | **0.4671** |
| pearson_manhattan | 0.4156 |
| spearman_manhattan | 0.456 |
| pearson_euclidean | 0.4167 |
| spearman_euclidean | 0.4575 |
| pearson_dot | 0.3753 |
| spearman_dot | 0.3629 |
| pearson_max | 0.4412 |
| spearman_max | 0.4671 |
<!--
## 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: 8,081,275 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: 23 tokens</li><li>mean: 31.48 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 30.06 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.44</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:--------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:---------------------------------|
| <code>T L F DUMMY PELVIS VERT Dynamic Seat Sled Test 2025 U718 G-S</code> | <code>T SCS R2 HY REF 059 R C PLR REF Y SM LAT 90 Deg / Left Side Decel-4g 2020 CX483 G-S</code> | <code>0.21129386503072142</code> |
| <code>T L F DUMMY PELVIS VERT Dynamic Seat Sled Test 2025 U718 G-S</code> | <code>T R F DUMMY PELVIS VERT 75 Deg Oblique Right Side 10 in. Pole 2015 P552 G-S</code> | <code>0.4972955033248179</code> |
| <code>T L F DUMMY PELVIS VERT Dynamic Seat Sled Test 2025 U718 G-S</code> | <code>T SCS L1 HY REF 053 L B PLR REF Y SM LAT 90 Deg Front Bumper Override 2021 CX727 G-S</code> | <code>0.5701051768787058</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
#### Unnamed Dataset
* Size: 1,726,581 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: 22 tokens</li><li>mean: 25.0 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 31.04 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.44</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:---------------------------------|
| <code>T R F ADAPTIVE TETHER VENT SQUIB VOLT 30 Deg Front Angular Right 20xx GENERIC VOLTS</code> | <code>T L F DUMMY T12 LONG 27 Deg Crabbed Left Side NHTSA 214 MDB to vehicle 2015 P552 G-S</code> | <code>0.6835618484879796</code> |
| <code>T R F ADAPTIVE TETHER VENT SQUIB VOLT 30 Deg Front Angular Right 20xx GENERIC VOLTS</code> | <code>T L F DUMMY R FEMUR LONG 90 Deg Front 2022 U553 G-S</code> | <code>0.666531064739</code> |
| <code>T R F ADAPTIVE TETHER VENT SQUIB VOLT 30 Deg Front Angular Right 20xx GENERIC VOLTS</code> | <code>T R F DUMMY NECK UPPER MZ LOAD 90 Deg Front 2019 P375ICA IN-LBS</code> | <code>0.46391834212079874</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
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 3e-05
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1
- `fp16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 3e-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`: 4
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `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
- `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`: 4
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 0
- `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_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: 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
- `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
- `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`: False
- `include_tokens_per_second`: False
- `neftune_noise_alpha`: None
- `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 |
|:------:|:------:|:-------------:|:------:|:-----------------------:|
| 0.0317 | 1000 | 6.3069 | - | - |
| 0.0634 | 2000 | 6.1793 | - | - |
| 0.0950 | 3000 | 6.1607 | - | - |
| 0.1267 | 4000 | 6.1512 | - | - |
| 0.1584 | 5000 | 6.1456 | - | - |
| 0.1901 | 6000 | 6.1419 | - | - |
| 0.2218 | 7000 | 6.1398 | - | - |
| 0.2534 | 8000 | 6.1377 | - | - |
| 0.2851 | 9000 | 6.1352 | - | - |
| 0.3168 | 10000 | 6.1338 | - | - |
| 0.3485 | 11000 | 6.1332 | - | - |
| 0.3801 | 12000 | 6.1309 | - | - |
| 0.4118 | 13000 | 6.1315 | - | - |
| 0.4435 | 14000 | 6.1283 | - | - |
| 0.4752 | 15000 | 6.129 | - | - |
| 0.5069 | 16000 | 6.1271 | - | - |
| 0.5385 | 17000 | 6.1265 | - | - |
| 0.5702 | 18000 | 6.1238 | - | - |
| 0.6019 | 19000 | 6.1234 | - | - |
| 0.6336 | 20000 | 6.1225 | - | - |
| 0.6653 | 21000 | 6.1216 | - | - |
| 0.6969 | 22000 | 6.1196 | - | - |
| 0.7286 | 23000 | 6.1198 | - | - |
| 0.7603 | 24000 | 6.1178 | - | - |
| 0.7920 | 25000 | 6.117 | - | - |
| 0.8236 | 26000 | 6.1167 | - | - |
| 0.8553 | 27000 | 6.1165 | - | - |
| 0.8870 | 28000 | 6.1149 | - | - |
| 0.9187 | 29000 | 6.1146 | - | - |
| 0.9504 | 30000 | 6.113 | - | - |
| 0.9820 | 31000 | 6.1143 | - | - |
| 1.0 | 31567 | - | 6.1150 | 0.4829 |
| 1.0137 | 32000 | 6.1115 | - | - |
| 1.0454 | 33000 | 6.111 | - | - |
| 1.0771 | 34000 | 6.1091 | - | - |
| 1.1088 | 35000 | 6.1094 | - | - |
| 1.1404 | 36000 | 6.1078 | - | - |
| 1.1721 | 37000 | 6.1095 | - | - |
| 1.2038 | 38000 | 6.106 | - | - |
| 1.2355 | 39000 | 6.1071 | - | - |
| 1.2671 | 40000 | 6.1073 | - | - |
| 1.2988 | 41000 | 6.1064 | - | - |
| 1.3305 | 42000 | 6.1047 | - | - |
| 1.3622 | 43000 | 6.1054 | - | - |
| 1.3939 | 44000 | 6.1048 | - | - |
| 1.4255 | 45000 | 6.1053 | - | - |
| 1.4572 | 46000 | 6.1058 | - | - |
| 1.4889 | 47000 | 6.1037 | - | - |
| 1.5206 | 48000 | 6.1041 | - | - |
| 1.5523 | 49000 | 6.1023 | - | - |
| 1.5839 | 50000 | 6.1018 | - | - |
| 1.6156 | 51000 | 6.104 | - | - |
| 1.6473 | 52000 | 6.1004 | - | - |
| 1.6790 | 53000 | 6.1027 | - | - |
| 1.7106 | 54000 | 6.1017 | - | - |
| 1.7423 | 55000 | 6.1011 | - | - |
| 1.7740 | 56000 | 6.1002 | - | - |
| 1.8057 | 57000 | 6.0994 | - | - |
| 1.8374 | 58000 | 6.0985 | - | - |
| 1.8690 | 59000 | 6.0986 | - | - |
| 1.9007 | 60000 | 6.1006 | - | - |
| 1.9324 | 61000 | 6.0983 | - | - |
| 1.9641 | 62000 | 6.0983 | - | - |
| 1.9958 | 63000 | 6.0973 | - | - |
| 2.0 | 63134 | - | 6.1193 | 0.4828 |
| 2.0274 | 64000 | 6.0943 | - | - |
| 2.0591 | 65000 | 6.0941 | - | - |
| 2.0908 | 66000 | 6.0936 | - | - |
| 2.1225 | 67000 | 6.0909 | - | - |
| 2.1541 | 68000 | 6.0925 | - | - |
| 2.1858 | 69000 | 6.0932 | - | - |
| 2.2175 | 70000 | 6.0939 | - | - |
| 2.2492 | 71000 | 6.0919 | - | - |
| 2.2809 | 72000 | 6.0932 | - | - |
| 2.3125 | 73000 | 6.0916 | - | - |
| 2.3442 | 74000 | 6.0919 | - | - |
| 2.3759 | 75000 | 6.0919 | - | - |
| 2.4076 | 76000 | 6.0911 | - | - |
| 2.4393 | 77000 | 6.0924 | - | - |
| 2.4709 | 78000 | 6.0911 | - | - |
| 2.5026 | 79000 | 6.0922 | - | - |
| 2.5343 | 80000 | 6.0926 | - | - |
| 2.5660 | 81000 | 6.0911 | - | - |
| 2.5976 | 82000 | 6.0897 | - | - |
| 2.6293 | 83000 | 6.0922 | - | - |
| 2.6610 | 84000 | 6.0908 | - | - |
| 2.6927 | 85000 | 6.0884 | - | - |
| 2.7244 | 86000 | 6.0907 | - | - |
| 2.7560 | 87000 | 6.0904 | - | - |
| 2.7877 | 88000 | 6.0881 | - | - |
| 2.8194 | 89000 | 6.0902 | - | - |
| 2.8511 | 90000 | 6.088 | - | - |
| 2.8828 | 91000 | 6.0888 | - | - |
| 2.9144 | 92000 | 6.0884 | - | - |
| 2.9461 | 93000 | 6.0881 | - | - |
| 2.9778 | 94000 | 6.0896 | - | - |
| 3.0 | 94701 | - | 6.1225 | 0.4788 |
| 3.0095 | 95000 | 6.0857 | - | - |
| 3.0412 | 96000 | 6.0838 | - | - |
| 3.0728 | 97000 | 6.0843 | - | - |
| 3.1045 | 98000 | 6.0865 | - | - |
| 3.1362 | 99000 | 6.0827 | - | - |
| 3.1679 | 100000 | 6.0836 | - | - |
| 3.1995 | 101000 | 6.0837 | - | - |
| 3.2312 | 102000 | 6.0836 | - | - |
| 3.2629 | 103000 | 6.0837 | - | - |
| 3.2946 | 104000 | 6.084 | - | - |
| 3.3263 | 105000 | 6.0836 | - | - |
| 3.3579 | 106000 | 6.0808 | - | - |
| 3.3896 | 107000 | 6.0821 | - | - |
| 3.4213 | 108000 | 6.0817 | - | - |
| 3.4530 | 109000 | 6.082 | - | - |
| 3.4847 | 110000 | 6.083 | - | - |
| 3.5163 | 111000 | 6.0829 | - | - |
| 3.5480 | 112000 | 6.0832 | - | - |
| 3.5797 | 113000 | 6.0829 | - | - |
| 3.6114 | 114000 | 6.0837 | - | - |
| 3.6430 | 115000 | 6.082 | - | - |
| 3.6747 | 116000 | 6.0823 | - | - |
| 3.7064 | 117000 | 6.082 | - | - |
| 3.7381 | 118000 | 6.0833 | - | - |
| 3.7698 | 119000 | 6.0831 | - | - |
| 3.8014 | 120000 | 6.0814 | - | - |
| 3.8331 | 121000 | 6.0813 | - | - |
| 3.8648 | 122000 | 6.0797 | - | - |
| 3.8965 | 123000 | 6.0793 | - | - |
| 3.9282 | 124000 | 6.0818 | - | - |
| 3.9598 | 125000 | 6.0806 | - | - |
| 3.9915 | 126000 | 6.08 | - | - |
| 4.0 | 126268 | - | 6.1266 | 0.4671 |
</details>
### Framework Versions
- Python: 3.10.6
- Sentence Transformers: 3.0.0
- Transformers: 4.35.0
- PyTorch: 2.1.0a0+4136153
- Accelerate: 0.30.1
- Datasets: 2.14.1
- Tokenizers: 0.14.1
## 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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