sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:9504 loss:TripletLoss arxiv:1908.10084 arxiv:1703.07737 base_model:dbourget/pb-small-10e-tsdae6e-philsim-cosine-3e-pt1 base_model:finetune:dbourget/pb-small-10e-tsdae6e-philsim-cosine-3e-pt1 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: dbourget/pb-small-10e-tsdae6e-philsim-cosine-3e-pt1
library_name: sentence-transformers
metrics:
- cosine_accuracy
- dot_accuracy
- manhattan_accuracy
- euclidean_accuracy
- max_accuracy
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:9504
- loss:TripletLoss
widget:
- source_sentence: cap product
  sentences:
  - method of adjoining a chain of degree p with a co-chain of degree q, where q is
    less than or equal to p, to form a composite chain of degree p-q
  - 'Ontology '
  - hat commodity
- source_sentence: cognitivism
  sentences:
  - supporting cognitive science
  - study of changes in organisms caused by modification of gene expression rather
    than alteration of the genetic code
  - 'the idea that mind works like an algorithmic symbol manipulation '
- source_sentence: doxastic voluntarism
  sentences:
  - Land surrounded by water
  - belief one is free
  - the ability to will beliefs
- source_sentence: conceptual role
  sentences:
  - concept
  - inferential role
  - 'Theory of knowledge '
- source_sentence: scientific revolutions
  sentences:
  - scientific realism
  - Universal moral principles govern legal systems
  - paradigm shifts
model-index:
- name: SentenceTransformer based on dbourget/pb-small-10e-tsdae6e-philsim-cosine-3e-pt1
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: beatai dev
      type: beatai-dev
    metrics:
    - type: cosine_accuracy
      value: 0.7929292929292929
      name: Cosine Accuracy
    - type: dot_accuracy
      value: 0.2542087542087542
      name: Dot Accuracy
    - type: manhattan_accuracy
      value: 0.8021885521885522
      name: Manhattan Accuracy
    - type: euclidean_accuracy
      value: 0.8013468013468014
      name: Euclidean Accuracy
    - type: max_accuracy
      value: 0.8021885521885522
      name: Max Accuracy
---

# SentenceTransformer based on dbourget/pb-small-10e-tsdae6e-philsim-cosine-3e-pt1

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [dbourget/pb-small-10e-tsdae6e-philsim-cosine-3e-pt1](https://huggingface.co/dbourget/pb-small-10e-tsdae6e-philsim-cosine-3e-pt1). It maps sentences & paragraphs to a 1024-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:** [dbourget/pb-small-10e-tsdae6e-philsim-cosine-3e-pt1](https://huggingface.co/dbourget/pb-small-10e-tsdae6e-philsim-cosine-3e-pt1) <!-- at revision e3be09e156ca8e2b7b4e5d296fc50a316393eda3 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 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: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("dbourget/pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-cosine-50e")
# Run inference
sentences = [
    'scientific revolutions',
    'paradigm shifts',
    'scientific realism',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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: `beatai-dev`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.7929** |
| dot_accuracy        | 0.2542     |
| manhattan_accuracy  | 0.8022     |
| euclidean_accuracy  | 0.8013     |
| max_accuracy        | 0.8022     |

<!--
## 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 Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 138
- `per_device_eval_batch_size`: 138
- `learning_rate`: 5e-07
- `weight_decay`: 0.01
- `num_train_epochs`: 50
- `lr_scheduler_type`: constant
- `bf16`: True
- `dataloader_drop_last`: True
- `resume_from_checkpoint`: 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`: 138
- `per_device_eval_batch_size`: 138
- `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-07
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 50
- `max_steps`: -1
- `lr_scheduler_type`: constant
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0
- `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`: 2
- `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`: True
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
<details><summary>Click to expand</summary>

| Epoch   | Step | Training Loss | loss   | beatai-dev_cosine_accuracy |
|:-------:|:----:|:-------------:|:------:|:--------------------------:|
| 0       | 0    | -             | -      | 0.4764                     |
| 0.1471  | 10   | 0.2061        | -      | -                          |
| 0.2941  | 20   | 0.2048        | -      | -                          |
| 0.4412  | 30   | 0.204         | -      | -                          |
| 0.5882  | 40   | 0.202         | -      | -                          |
| 0.7353  | 50   | 0.2019        | 0.2010 | 0.5219                     |
| 0.8824  | 60   | 0.2017        | -      | -                          |
| 1.0294  | 70   | 0.1954        | -      | -                          |
| 1.1765  | 80   | 0.1959        | -      | -                          |
| 1.3235  | 90   | 0.1941        | -      | -                          |
| 1.4706  | 100  | 0.1937        | 0.1929 | 0.5598                     |
| 1.6176  | 110  | 0.1923        | -      | -                          |
| 1.7647  | 120  | 0.1893        | -      | -                          |
| 1.9118  | 130  | 0.1861        | -      | -                          |
| 2.0588  | 140  | 0.1842        | -      | -                          |
| 2.2059  | 150  | 0.1818        | 0.1814 | 0.5985                     |
| 2.3529  | 160  | 0.1834        | -      | -                          |
| 2.5     | 170  | 0.1729        | -      | -                          |
| 2.6471  | 180  | 0.1726        | -      | -                          |
| 2.7941  | 190  | 0.1668        | -      | -                          |
| 2.9412  | 200  | 0.1622        | 0.1653 | 0.6330                     |
| 3.0882  | 210  | 0.1604        | -      | -                          |
| 3.2353  | 220  | 0.1572        | -      | -                          |
| 3.3824  | 230  | 0.159         | -      | -                          |
| 3.5294  | 240  | 0.1567        | -      | -                          |
| 3.6765  | 250  | 0.1481        | 0.1562 | 0.6532                     |
| 3.8235  | 260  | 0.148         | -      | -                          |
| 3.9706  | 270  | 0.1492        | -      | -                          |
| 4.1176  | 280  | 0.1528        | -      | -                          |
| 4.2647  | 290  | 0.1437        | -      | -                          |
| 4.4118  | 300  | 0.1481        | 0.1490 | 0.6658                     |
| 4.5588  | 310  | 0.1386        | -      | -                          |
| 4.7059  | 320  | 0.1413        | -      | -                          |
| 4.8529  | 330  | 0.1407        | -      | -                          |
| 5.0     | 340  | 0.1387        | -      | -                          |
| 5.1471  | 350  | 0.1423        | 0.1438 | 0.6717                     |
| 5.2941  | 360  | 0.1376        | -      | -                          |
| 5.4412  | 370  | 0.1314        | -      | -                          |
| 5.5882  | 380  | 0.1416        | -      | -                          |
| 5.7353  | 390  | 0.1284        | -      | -                          |
| 5.8824  | 400  | 0.1375        | 0.1394 | 0.6801                     |
| 6.0294  | 410  | 0.1308        | -      | -                          |
| 6.1765  | 420  | 0.1286        | -      | -                          |
| 6.3235  | 430  | 0.1326        | -      | -                          |
| 6.4706  | 440  | 0.1356        | -      | -                          |
| 6.6176  | 450  | 0.1298        | 0.1361 | 0.6877                     |
| 6.7647  | 460  | 0.1242        | -      | -                          |
| 6.9118  | 470  | 0.1299        | -      | -                          |
| 7.0588  | 480  | 0.1279        | -      | -                          |
| 7.2059  | 490  | 0.1234        | -      | -                          |
| 7.3529  | 500  | 0.1298        | 0.1333 | 0.7045                     |
| 7.5     | 510  | 0.1252        | -      | -                          |
| 7.6471  | 520  | 0.1248        | -      | -                          |
| 7.7941  | 530  | 0.1241        | -      | -                          |
| 7.9412  | 540  | 0.126         | -      | -                          |
| 8.0882  | 550  | 0.1252        | 0.1316 | 0.7071                     |
| 8.2353  | 560  | 0.1237        | -      | -                          |
| 8.3824  | 570  | 0.1205        | -      | -                          |
| 8.5294  | 580  | 0.1195        | -      | -                          |
| 8.6765  | 590  | 0.1187        | -      | -                          |
| 8.8235  | 600  | 0.1187        | 0.1293 | 0.7138                     |
| 8.9706  | 610  | 0.1269        | -      | -                          |
| 9.1176  | 620  | 0.1261        | -      | -                          |
| 9.2647  | 630  | 0.1182        | -      | -                          |
| 9.4118  | 640  | 0.1219        | -      | -                          |
| 9.5588  | 650  | 0.1173        | 0.1276 | 0.7172                     |
| 9.7059  | 660  | 0.1182        | -      | -                          |
| 9.8529  | 670  | 0.122         | -      | -                          |
| 10.0    | 680  | 0.1179        | -      | -                          |
| 10.1471 | 690  | 0.1137        | -      | -                          |
| 10.2941 | 700  | 0.1248        | 0.1261 | 0.7247                     |
| 10.4412 | 710  | 0.1162        | -      | -                          |
| 10.5882 | 720  | 0.1166        | -      | -                          |
| 10.7353 | 730  | 0.1111        | -      | -                          |
| 10.8824 | 740  | 0.115         | -      | -                          |
| 11.0294 | 750  | 0.1175        | 0.1247 | 0.7298                     |
| 11.1765 | 760  | 0.1136        | -      | -                          |
| 11.3235 | 770  | 0.1172        | -      | -                          |
| 11.4706 | 780  | 0.1158        | -      | -                          |
| 11.6176 | 790  | 0.1142        | -      | -                          |
| 11.7647 | 800  | 0.1097        | 0.1236 | 0.7332                     |
| 11.9118 | 810  | 0.1161        | -      | -                          |
| 12.0588 | 820  | 0.1153        | -      | -                          |
| 12.2059 | 830  | 0.1114        | -      | -                          |
| 12.3529 | 840  | 0.1133        | -      | -                          |
| 12.5    | 850  | 0.1104        | 0.1226 | 0.7332                     |
| 12.6471 | 860  | 0.1093        | -      | -                          |
| 12.7941 | 870  | 0.1157        | -      | -                          |
| 12.9412 | 880  | 0.1127        | -      | -                          |
| 13.0882 | 890  | 0.1115        | -      | -                          |
| 13.2353 | 900  | 0.1109        | 0.1214 | 0.7323                     |
| 13.3824 | 910  | 0.1125        | -      | -                          |
| 13.5294 | 920  | 0.1097        | -      | -                          |
| 13.6765 | 930  | 0.1124        | -      | -                          |
| 13.8235 | 940  | 0.114         | -      | -                          |
| 13.9706 | 950  | 0.11          | 0.1204 | 0.7382                     |
| 14.1176 | 960  | 0.1049        | -      | -                          |
| 14.2647 | 970  | 0.1128        | -      | -                          |
| 14.4118 | 980  | 0.1109        | -      | -                          |
| 14.5588 | 990  | 0.1087        | -      | -                          |
| 14.7059 | 1000 | 0.1079        | 0.1196 | 0.7382                     |
| 14.8529 | 1010 | 0.1077        | -      | -                          |
| 15.0    | 1020 | 0.1061        | -      | -                          |
| 15.1471 | 1030 | 0.1101        | -      | -                          |
| 15.2941 | 1040 | 0.1087        | -      | -                          |
| 15.4412 | 1050 | 0.106         | 0.1186 | 0.7399                     |
| 15.5882 | 1060 | 0.1047        | -      | -                          |
| 15.7353 | 1070 | 0.1048        | -      | -                          |
| 15.8824 | 1080 | 0.103         | -      | -                          |
| 16.0294 | 1090 | 0.1064        | -      | -                          |
| 16.1765 | 1100 | 0.1029        | 0.1179 | 0.7433                     |
| 16.3235 | 1110 | 0.1033        | -      | -                          |
| 16.4706 | 1120 | 0.1066        | -      | -                          |
| 16.6176 | 1130 | 0.1095        | -      | -                          |
| 16.7647 | 1140 | 0.1031        | -      | -                          |
| 16.9118 | 1150 | 0.1           | 0.1172 | 0.7466                     |
| 17.0588 | 1160 | 0.1056        | -      | -                          |
| 17.2059 | 1170 | 0.1033        | -      | -                          |
| 17.3529 | 1180 | 0.102         | -      | -                          |
| 17.5    | 1190 | 0.1083        | -      | -                          |
| 17.6471 | 1200 | 0.0971        | 0.1164 | 0.7458                     |
| 17.7941 | 1210 | 0.1016        | -      | -                          |
| 17.9412 | 1220 | 0.1033        | -      | -                          |
| 18.0882 | 1230 | 0.0987        | -      | -                          |
| 18.2353 | 1240 | 0.1062        | -      | -                          |
| 18.3824 | 1250 | 0.0925        | 0.1157 | 0.7475                     |
| 18.5294 | 1260 | 0.1028        | -      | -                          |
| 18.6765 | 1270 | 0.1012        | -      | -                          |
| 18.8235 | 1280 | 0.1027        | -      | -                          |
| 18.9706 | 1290 | 0.1026        | -      | -                          |
| 19.1176 | 1300 | 0.1023        | 0.1148 | 0.7508                     |
| 19.2647 | 1310 | 0.1053        | -      | -                          |
| 19.4118 | 1320 | 0.0981        | -      | -                          |
| 19.5588 | 1330 | 0.0975        | -      | -                          |
| 19.7059 | 1340 | 0.1006        | -      | -                          |
| 19.8529 | 1350 | 0.0991        | 0.1141 | 0.7508                     |
| 20.0    | 1360 | 0.0994        | -      | -                          |
| 20.1471 | 1370 | 0.0998        | -      | -                          |
| 20.2941 | 1380 | 0.1014        | -      | -                          |
| 20.4412 | 1390 | 0.0986        | -      | -                          |
| 20.5882 | 1400 | 0.098         | 0.1133 | 0.7525                     |
| 20.7353 | 1410 | 0.101         | -      | -                          |
| 20.8824 | 1420 | 0.098         | -      | -                          |
| 21.0294 | 1430 | 0.1041        | -      | -                          |
| 21.1765 | 1440 | 0.0979        | -      | -                          |
| 21.3235 | 1450 | 0.1006        | 0.1126 | 0.7559                     |
| 21.4706 | 1460 | 0.097         | -      | -                          |
| 21.6176 | 1470 | 0.0985        | -      | -                          |
| 21.7647 | 1480 | 0.0956        | -      | -                          |
| 21.9118 | 1490 | 0.0993        | -      | -                          |
| 22.0588 | 1500 | 0.0943        | 0.1120 | 0.7551                     |
| 22.2059 | 1510 | 0.0977        | -      | -                          |
| 22.3529 | 1520 | 0.0998        | -      | -                          |
| 22.5    | 1530 | 0.0977        | -      | -                          |
| 22.6471 | 1540 | 0.099         | -      | -                          |
| 22.7941 | 1550 | 0.0925        | 0.1113 | 0.7576                     |
| 22.9412 | 1560 | 0.0929        | -      | -                          |
| 23.0882 | 1570 | 0.0965        | -      | -                          |
| 23.2353 | 1580 | 0.0896        | -      | -                          |
| 23.3824 | 1590 | 0.0993        | -      | -                          |
| 23.5294 | 1600 | 0.0941        | 0.1109 | 0.7576                     |
| 23.6765 | 1610 | 0.0927        | -      | -                          |
| 23.8235 | 1620 | 0.0994        | -      | -                          |
| 23.9706 | 1630 | 0.0956        | -      | -                          |
| 24.1176 | 1640 | 0.0947        | -      | -                          |
| 24.2647 | 1650 | 0.0927        | 0.1103 | 0.7576                     |
| 24.4118 | 1660 | 0.0935        | -      | -                          |
| 24.5588 | 1670 | 0.0996        | -      | -                          |
| 24.7059 | 1680 | 0.0903        | -      | -                          |
| 24.8529 | 1690 | 0.0916        | -      | -                          |
| 25.0    | 1700 | 0.0951        | 0.1096 | 0.7584                     |
| 25.1471 | 1710 | 0.0924        | -      | -                          |
| 25.2941 | 1720 | 0.0952        | -      | -                          |
| 25.4412 | 1730 | 0.0954        | -      | -                          |
| 25.5882 | 1740 | 0.0968        | -      | -                          |
| 25.7353 | 1750 | 0.0942        | 0.1090 | 0.7593                     |
| 25.8824 | 1760 | 0.0913        | -      | -                          |
| 26.0294 | 1770 | 0.0931        | -      | -                          |
| 26.1765 | 1780 | 0.0872        | -      | -                          |
| 26.3235 | 1790 | 0.0915        | -      | -                          |
| 26.4706 | 1800 | 0.0937        | 0.1085 | 0.7601                     |
| 26.6176 | 1810 | 0.0971        | -      | -                          |
| 26.7647 | 1820 | 0.0944        | -      | -                          |
| 26.9118 | 1830 | 0.0908        | -      | -                          |
| 27.0588 | 1840 | 0.089         | -      | -                          |
| 27.2059 | 1850 | 0.0944        | 0.1082 | 0.7626                     |
| 27.3529 | 1860 | 0.0926        | -      | -                          |
| 27.5    | 1870 | 0.087         | -      | -                          |
| 27.6471 | 1880 | 0.0904        | -      | -                          |
| 27.7941 | 1890 | 0.0886        | -      | -                          |
| 27.9412 | 1900 | 0.0942        | 0.1077 | 0.7635                     |
| 28.0882 | 1910 | 0.0947        | -      | -                          |
| 28.2353 | 1920 | 0.0857        | -      | -                          |
| 28.3824 | 1930 | 0.0908        | -      | -                          |
| 28.5294 | 1940 | 0.0943        | -      | -                          |
| 28.6765 | 1950 | 0.0902        | 0.1071 | 0.7668                     |
| 28.8235 | 1960 | 0.0909        | -      | -                          |
| 28.9706 | 1970 | 0.0897        | -      | -                          |
| 29.1176 | 1980 | 0.0924        | -      | -                          |
| 29.2647 | 1990 | 0.0909        | -      | -                          |
| 29.4118 | 2000 | 0.0895        | 0.1066 | 0.7652                     |
| 29.5588 | 2010 | 0.0832        | -      | -                          |
| 29.7059 | 2020 | 0.0883        | -      | -                          |
| 29.8529 | 2030 | 0.0935        | -      | -                          |
| 30.0    | 2040 | 0.09          | -      | -                          |
| 30.1471 | 2050 | 0.0891        | 0.1060 | 0.7677                     |
| 30.2941 | 2060 | 0.0978        | -      | -                          |
| 30.4412 | 2070 | 0.0894        | -      | -                          |
| 30.5882 | 2080 | 0.0893        | -      | -                          |
| 30.7353 | 2090 | 0.0815        | -      | -                          |
| 30.8824 | 2100 | 0.0889        | 0.1058 | 0.7660                     |
| 31.0294 | 2110 | 0.0801        | -      | -                          |
| 31.1765 | 2120 | 0.0922        | -      | -                          |
| 31.3235 | 2130 | 0.0868        | -      | -                          |
| 31.4706 | 2140 | 0.0858        | -      | -                          |
| 31.6176 | 2150 | 0.0862        | 0.1055 | 0.7685                     |
| 31.7647 | 2160 | 0.0861        | -      | -                          |
| 31.9118 | 2170 | 0.0896        | -      | -                          |
| 32.0588 | 2180 | 0.0877        | -      | -                          |
| 32.2059 | 2190 | 0.0864        | -      | -                          |
| 32.3529 | 2200 | 0.0921        | 0.1050 | 0.7694                     |
| 32.5    | 2210 | 0.082         | -      | -                          |
| 32.6471 | 2220 | 0.0902        | -      | -                          |
| 32.7941 | 2230 | 0.0825        | -      | -                          |
| 32.9412 | 2240 | 0.0829        | -      | -                          |
| 33.0882 | 2250 | 0.0859        | 0.1046 | 0.7694                     |
| 33.2353 | 2260 | 0.0847        | -      | -                          |
| 33.3824 | 2270 | 0.0829        | -      | -                          |
| 33.5294 | 2280 | 0.0841        | -      | -                          |
| 33.6765 | 2290 | 0.0833        | -      | -                          |
| 33.8235 | 2300 | 0.0899        | 0.1042 | 0.7710                     |
| 33.9706 | 2310 | 0.0789        | -      | -                          |
| 34.1176 | 2320 | 0.0809        | -      | -                          |
| 34.2647 | 2330 | 0.0835        | -      | -                          |
| 34.4118 | 2340 | 0.0816        | -      | -                          |
| 34.5588 | 2350 | 0.0803        | 0.1038 | 0.7744                     |
| 34.7059 | 2360 | 0.0808        | -      | -                          |
| 34.8529 | 2370 | 0.0867        | -      | -                          |
| 35.0    | 2380 | 0.0878        | -      | -                          |
| 35.1471 | 2390 | 0.0869        | -      | -                          |
| 35.2941 | 2400 | 0.0785        | 0.1034 | 0.7753                     |
| 35.4412 | 2410 | 0.0849        | -      | -                          |
| 35.5882 | 2420 | 0.0832        | -      | -                          |
| 35.7353 | 2430 | 0.0799        | -      | -                          |
| 35.8824 | 2440 | 0.0813        | -      | -                          |
| 36.0294 | 2450 | 0.0801        | 0.1029 | 0.7753                     |
| 36.1765 | 2460 | 0.0771        | -      | -                          |
| 36.3235 | 2470 | 0.0828        | -      | -                          |
| 36.4706 | 2480 | 0.0837        | -      | -                          |
| 36.6176 | 2490 | 0.0774        | -      | -                          |
| 36.7647 | 2500 | 0.0822        | 0.1026 | 0.7769                     |
| 36.9118 | 2510 | 0.0845        | -      | -                          |
| 37.0588 | 2520 | 0.0882        | -      | -                          |
| 37.2059 | 2530 | 0.0802        | -      | -                          |
| 37.3529 | 2540 | 0.0806        | -      | -                          |
| 37.5    | 2550 | 0.0809        | 0.1022 | 0.7795                     |
| 37.6471 | 2560 | 0.0806        | -      | -                          |
| 37.7941 | 2570 | 0.0788        | -      | -                          |
| 37.9412 | 2580 | 0.0858        | -      | -                          |
| 38.0882 | 2590 | 0.0791        | -      | -                          |
| 38.2353 | 2600 | 0.0842        | 0.1018 | 0.7795                     |
| 38.3824 | 2610 | 0.0799        | -      | -                          |
| 38.5294 | 2620 | 0.0769        | -      | -                          |
| 38.6765 | 2630 | 0.0823        | -      | -                          |
| 38.8235 | 2640 | 0.0784        | -      | -                          |
| 38.9706 | 2650 | 0.0863        | 0.1016 | 0.7795                     |
| 39.1176 | 2660 | 0.0751        | -      | -                          |
| 39.2647 | 2670 | 0.0847        | -      | -                          |
| 39.4118 | 2680 | 0.0784        | -      | -                          |
| 39.5588 | 2690 | 0.0799        | -      | -                          |
| 39.7059 | 2700 | 0.0771        | 0.1013 | 0.7811                     |
| 39.8529 | 2710 | 0.0763        | -      | -                          |
| 40.0    | 2720 | 0.0783        | -      | -                          |
| 40.1471 | 2730 | 0.0784        | -      | -                          |
| 40.2941 | 2740 | 0.0761        | -      | -                          |
| 40.4412 | 2750 | 0.0797        | 0.1011 | 0.7837                     |
| 40.5882 | 2760 | 0.0809        | -      | -                          |
| 40.7353 | 2770 | 0.0758        | -      | -                          |
| 40.8824 | 2780 | 0.0777        | -      | -                          |
| 41.0294 | 2790 | 0.0777        | -      | -                          |
| 41.1765 | 2800 | 0.0806        | 0.1006 | 0.7786                     |
| 41.3235 | 2810 | 0.0852        | -      | -                          |
| 41.4706 | 2820 | 0.079         | -      | -                          |
| 41.6176 | 2830 | 0.0749        | -      | -                          |
| 41.7647 | 2840 | 0.0805        | -      | -                          |
| 41.9118 | 2850 | 0.0779        | 0.1003 | 0.7854                     |
| 42.0588 | 2860 | 0.0759        | -      | -                          |
| 42.2059 | 2870 | 0.0794        | -      | -                          |
| 42.3529 | 2880 | 0.0811        | -      | -                          |
| 42.5    | 2890 | 0.0772        | -      | -                          |
| 42.6471 | 2900 | 0.0757        | 0.1001 | 0.7828                     |
| 42.7941 | 2910 | 0.0781        | -      | -                          |
| 42.9412 | 2920 | 0.0751        | -      | -                          |
| 43.0882 | 2930 | 0.0752        | -      | -                          |
| 43.2353 | 2940 | 0.079         | -      | -                          |
| 43.3824 | 2950 | 0.076         | 0.0997 | 0.7811                     |
| 43.5294 | 2960 | 0.0783        | -      | -                          |
| 43.6765 | 2970 | 0.0774        | -      | -                          |
| 43.8235 | 2980 | 0.07          | -      | -                          |
| 43.9706 | 2990 | 0.073         | -      | -                          |
| 44.1176 | 3000 | 0.0762        | 0.0993 | 0.7854                     |
| 44.2647 | 3010 | 0.0749        | -      | -                          |
| 44.4118 | 3020 | 0.0782        | -      | -                          |
| 44.5588 | 3030 | 0.0764        | -      | -                          |
| 44.7059 | 3040 | 0.0759        | -      | -                          |
| 44.8529 | 3050 | 0.0769        | 0.0991 | 0.7887                     |
| 45.0    | 3060 | 0.0754        | -      | -                          |
| 45.1471 | 3070 | 0.0744        | -      | -                          |
| 45.2941 | 3080 | 0.0767        | -      | -                          |
| 45.4412 | 3090 | 0.0724        | -      | -                          |
| 45.5882 | 3100 | 0.0742        | 0.0989 | 0.7870                     |
| 45.7353 | 3110 | 0.0745        | -      | -                          |
| 45.8824 | 3120 | 0.076         | -      | -                          |
| 46.0294 | 3130 | 0.0666        | -      | -                          |
| 46.1765 | 3140 | 0.0801        | -      | -                          |
| 46.3235 | 3150 | 0.0734        | 0.0985 | 0.7887                     |
| 46.4706 | 3160 | 0.0703        | -      | -                          |
| 46.6176 | 3170 | 0.0772        | -      | -                          |
| 46.7647 | 3180 | 0.0763        | -      | -                          |
| 46.9118 | 3190 | 0.0718        | -      | -                          |
| 47.0588 | 3200 | 0.0724        | 0.0981 | 0.7904                     |
| 47.2059 | 3210 | 0.0755        | -      | -                          |
| 47.3529 | 3220 | 0.0719        | -      | -                          |
| 47.5    | 3230 | 0.0742        | -      | -                          |
| 47.6471 | 3240 | 0.074         | -      | -                          |
| 47.7941 | 3250 | 0.0758        | 0.0980 | 0.7921                     |
| 47.9412 | 3260 | 0.0727        | -      | -                          |
| 48.0882 | 3270 | 0.0676        | -      | -                          |
| 48.2353 | 3280 | 0.0791        | -      | -                          |
| 48.3824 | 3290 | 0.0751        | -      | -                          |
| 48.5294 | 3300 | 0.075         | 0.0977 | 0.7887                     |
| 48.6765 | 3310 | 0.0738        | -      | -                          |
| 48.8235 | 3320 | 0.0689        | -      | -                          |
| 48.9706 | 3330 | 0.0706        | -      | -                          |
| 49.1176 | 3340 | 0.0671        | -      | -                          |
| 49.2647 | 3350 | 0.0744        | 0.0974 | 0.7971                     |
| 49.4118 | 3360 | 0.0739        | -      | -                          |
| 49.5588 | 3370 | 0.0721        | -      | -                          |
| 49.7059 | 3380 | 0.073         | -      | -                          |
| 49.8529 | 3390 | 0.0707        | -      | -                          |
| 50.0    | 3400 | 0.0689        | 0.0972 | 0.7929                     |

</details>

### Framework Versions
- Python: 3.8.18
- Sentence Transformers: 3.1.1
- Transformers: 4.45.1
- PyTorch: 1.13.1+cu117
- Accelerate: 0.34.2
- Datasets: 3.0.0
- Tokenizers: 0.20.0

## 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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