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-6e-beatai-cosine-50e base_model:finetune:dbourget/pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-cosine-50e model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: dbourget/pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-cosine-50e
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-6e-beatai-cosine-50e
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: beatai dev
      type: beatai-dev
    metrics:
    - type: cosine_accuracy
      value: 0.813973063973064
      name: Cosine Accuracy
    - type: dot_accuracy
      value: 0.22727272727272727
      name: Dot Accuracy
    - type: manhattan_accuracy
      value: 0.8198653198653199
      name: Manhattan Accuracy
    - type: euclidean_accuracy
      value: 0.8156565656565656
      name: Euclidean Accuracy
    - type: max_accuracy
      value: 0.8198653198653199
      name: Max Accuracy
---

# SentenceTransformer based on dbourget/pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-cosine-50e

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [dbourget/pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-cosine-50e](https://huggingface.co/dbourget/pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-cosine-50e). 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-6e-beatai-cosine-50e](https://huggingface.co/dbourget/pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-cosine-50e) <!-- at revision 86e3b91181f7c10aa5a92184184dc50f0f25aa57 -->
- **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-80e")
# 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** | **0.814** |
| dot_accuracy        | 0.2273    |
| manhattan_accuracy  | 0.8199    |
| euclidean_accuracy  | 0.8157    |
| max_accuracy        | 0.8199    |

<!--
## 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`: 30
- `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`: 30
- `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    | -             | -      | 0.7904                     |
| 0.1471  | 10   | 0.0721        | -      | -                          |
| 0.2941  | 20   | 0.0708        | -      | -                          |
| 0.4412  | 30   | 0.0736        | -      | -                          |
| 0.5882  | 40   | 0.0704        | -      | -                          |
| 0.7353  | 50   | 0.0732        | 0.0971 | 0.7929                     |
| 0.8824  | 60   | 0.0716        | -      | -                          |
| 1.0294  | 70   | 0.0665        | -      | -                          |
| 1.1765  | 80   | 0.0698        | -      | -                          |
| 1.3235  | 90   | 0.0699        | -      | -                          |
| 1.4706  | 100  | 0.0691        | 0.0968 | 0.7912                     |
| 1.6176  | 110  | 0.0687        | -      | -                          |
| 1.7647  | 120  | 0.0701        | -      | -                          |
| 1.9118  | 130  | 0.0689        | -      | -                          |
| 2.0588  | 140  | 0.0696        | -      | -                          |
| 2.2059  | 150  | 0.071         | 0.0966 | 0.7929                     |
| 2.3529  | 160  | 0.078         | -      | -                          |
| 2.5     | 170  | 0.0675        | -      | -                          |
| 2.6471  | 180  | 0.065         | -      | -                          |
| 2.7941  | 190  | 0.0684        | -      | -                          |
| 2.9412  | 200  | 0.0689        | 0.0963 | 0.7938                     |
| 3.0882  | 210  | 0.0736        | -      | -                          |
| 3.2353  | 220  | 0.0684        | -      | -                          |
| 3.3824  | 230  | 0.0669        | -      | -                          |
| 3.5294  | 240  | 0.0688        | -      | -                          |
| 3.6765  | 250  | 0.0678        | 0.0959 | 0.7963                     |
| 3.8235  | 260  | 0.0682        | -      | -                          |
| 3.9706  | 270  | 0.0678        | -      | -                          |
| 4.1176  | 280  | 0.0686        | -      | -                          |
| 4.2647  | 290  | 0.0664        | -      | -                          |
| 4.4118  | 300  | 0.0703        | 0.0957 | 0.7980                     |
| 4.5588  | 310  | 0.065         | -      | -                          |
| 4.7059  | 320  | 0.0719        | -      | -                          |
| 4.8529  | 330  | 0.0685        | -      | -                          |
| 5.0     | 340  | 0.0639        | -      | -                          |
| 5.1471  | 350  | 0.0667        | 0.0957 | 0.7971                     |
| 5.2941  | 360  | 0.0661        | -      | -                          |
| 5.4412  | 370  | 0.0678        | -      | -                          |
| 5.5882  | 380  | 0.0725        | -      | -                          |
| 5.7353  | 390  | 0.0655        | -      | -                          |
| 5.8824  | 400  | 0.0649        | 0.0953 | 0.7980                     |
| 6.0294  | 410  | 0.0661        | -      | -                          |
| 6.1765  | 420  | 0.0662        | -      | -                          |
| 6.3235  | 430  | 0.0671        | -      | -                          |
| 6.4706  | 440  | 0.0698        | -      | -                          |
| 6.6176  | 450  | 0.0636        | 0.0951 | 0.7980                     |
| 6.7647  | 460  | 0.0644        | -      | -                          |
| 6.9118  | 470  | 0.0633        | -      | -                          |
| 7.0588  | 480  | 0.0679        | -      | -                          |
| 7.2059  | 490  | 0.067         | -      | -                          |
| 7.3529  | 500  | 0.0713        | 0.0948 | 0.7963                     |
| 7.5     | 510  | 0.0677        | -      | -                          |
| 7.6471  | 520  | 0.0666        | -      | -                          |
| 7.7941  | 530  | 0.065         | -      | -                          |
| 7.9412  | 540  | 0.0665        | -      | -                          |
| 8.0882  | 550  | 0.0656        | 0.0946 | 0.7963                     |
| 8.2353  | 560  | 0.0649        | -      | -                          |
| 8.3824  | 570  | 0.0649        | -      | -                          |
| 8.5294  | 580  | 0.0653        | -      | -                          |
| 8.6765  | 590  | 0.0648        | -      | -                          |
| 8.8235  | 600  | 0.0622        | 0.0944 | 0.7946                     |
| 8.9706  | 610  | 0.0689        | -      | -                          |
| 9.1176  | 620  | 0.0711        | -      | -                          |
| 9.2647  | 630  | 0.0611        | -      | -                          |
| 9.4118  | 640  | 0.0697        | -      | -                          |
| 9.5588  | 650  | 0.0645        | 0.0942 | 0.7963                     |
| 9.7059  | 660  | 0.0639        | -      | -                          |
| 9.8529  | 670  | 0.0643        | -      | -                          |
| 10.0    | 680  | 0.0644        | -      | -                          |
| 10.1471 | 690  | 0.0599        | -      | -                          |
| 10.2941 | 700  | 0.0723        | 0.0940 | 0.7955                     |
| 10.4412 | 710  | 0.0652        | -      | -                          |
| 10.5882 | 720  | 0.0646        | -      | -                          |
| 10.7353 | 730  | 0.0602        | -      | -                          |
| 10.8824 | 740  | 0.0644        | -      | -                          |
| 11.0294 | 750  | 0.066         | 0.0938 | 0.7971                     |
| 11.1765 | 760  | 0.0624        | -      | -                          |
| 11.3235 | 770  | 0.0652        | -      | -                          |
| 11.4706 | 780  | 0.0649        | -      | -                          |
| 11.6176 | 790  | 0.0624        | -      | -                          |
| 11.7647 | 800  | 0.0626        | 0.0937 | 0.7988                     |
| 11.9118 | 810  | 0.0635        | -      | -                          |
| 12.0588 | 820  | 0.0643        | -      | -                          |
| 12.2059 | 830  | 0.0663        | -      | -                          |
| 12.3529 | 840  | 0.0641        | -      | -                          |
| 12.5    | 850  | 0.0614        | 0.0933 | 0.8005                     |
| 12.6471 | 860  | 0.0613        | -      | -                          |
| 12.7941 | 870  | 0.0648        | -      | -                          |
| 12.9412 | 880  | 0.065         | -      | -                          |
| 13.0882 | 890  | 0.0589        | -      | -                          |
| 13.2353 | 900  | 0.0632        | 0.0931 | 0.7997                     |
| 13.3824 | 910  | 0.0649        | -      | -                          |
| 13.5294 | 920  | 0.0612        | -      | -                          |
| 13.6765 | 930  | 0.0634        | -      | -                          |
| 13.8235 | 940  | 0.0637        | -      | -                          |
| 13.9706 | 950  | 0.0626        | 0.0930 | 0.7997                     |
| 14.1176 | 960  | 0.0593        | -      | -                          |
| 14.2647 | 970  | 0.0662        | -      | -                          |
| 14.4118 | 980  | 0.0644        | -      | -                          |
| 14.5588 | 990  | 0.0582        | -      | -                          |
| 14.7059 | 1000 | 0.0626        | 0.0927 | 0.8013                     |
| 14.8529 | 1010 | 0.0605        | -      | -                          |
| 15.0    | 1020 | 0.0615        | -      | -                          |
| 15.1471 | 1030 | 0.0676        | -      | -                          |
| 15.2941 | 1040 | 0.0633        | -      | -                          |
| 15.4412 | 1050 | 0.06          | 0.0927 | 0.8047                     |
| 15.5882 | 1060 | 0.0572        | -      | -                          |
| 15.7353 | 1070 | 0.0579        | -      | -                          |
| 15.8824 | 1080 | 0.0594        | -      | -                          |
| 16.0294 | 1090 | 0.063         | -      | -                          |
| 16.1765 | 1100 | 0.0581        | 0.0927 | 0.8030                     |
| 16.3235 | 1110 | 0.0564        | -      | -                          |
| 16.4706 | 1120 | 0.0632        | -      | -                          |
| 16.6176 | 1130 | 0.065         | -      | -                          |
| 16.7647 | 1140 | 0.0602        | -      | -                          |
| 16.9118 | 1150 | 0.0581        | 0.0926 | 0.8039                     |
| 17.0588 | 1160 | 0.0623        | -      | -                          |
| 17.2059 | 1170 | 0.06          | -      | -                          |
| 17.3529 | 1180 | 0.0562        | -      | -                          |
| 17.5    | 1190 | 0.0627        | -      | -                          |
| 17.6471 | 1200 | 0.056         | 0.0924 | 0.8013                     |
| 17.7941 | 1210 | 0.0586        | -      | -                          |
| 17.9412 | 1220 | 0.0576        | -      | -                          |
| 18.0882 | 1230 | 0.056         | -      | -                          |
| 18.2353 | 1240 | 0.0611        | -      | -                          |
| 18.3824 | 1250 | 0.0551        | 0.0922 | 0.8047                     |
| 18.5294 | 1260 | 0.058         | -      | -                          |
| 18.6765 | 1270 | 0.0571        | -      | -                          |
| 18.8235 | 1280 | 0.0616        | -      | -                          |
| 18.9706 | 1290 | 0.0599        | -      | -                          |
| 19.1176 | 1300 | 0.0604        | 0.0920 | 0.8081                     |
| 19.2647 | 1310 | 0.0633        | -      | -                          |
| 19.4118 | 1320 | 0.0573        | -      | -                          |
| 19.5588 | 1330 | 0.0549        | -      | -                          |
| 19.7059 | 1340 | 0.0591        | -      | -                          |
| 19.8529 | 1350 | 0.0585        | 0.0918 | 0.8089                     |
| 20.0    | 1360 | 0.057         | -      | -                          |
| 20.1471 | 1370 | 0.057         | -      | -                          |
| 20.2941 | 1380 | 0.0625        | -      | -                          |
| 20.4412 | 1390 | 0.0589        | -      | -                          |
| 20.5882 | 1400 | 0.0577        | 0.0918 | 0.8098                     |
| 20.7353 | 1410 | 0.0583        | -      | -                          |
| 20.8824 | 1420 | 0.0567        | -      | -                          |
| 21.0294 | 1430 | 0.0619        | -      | -                          |
| 21.1765 | 1440 | 0.0572        | -      | -                          |
| 21.3235 | 1450 | 0.0594        | 0.0917 | 0.8123                     |
| 21.4706 | 1460 | 0.0567        | -      | -                          |
| 21.6176 | 1470 | 0.0611        | -      | -                          |
| 21.7647 | 1480 | 0.0533        | -      | -                          |
| 21.9118 | 1490 | 0.0595        | -      | -                          |
| 22.0588 | 1500 | 0.0521        | 0.0913 | 0.8114                     |
| 22.2059 | 1510 | 0.0586        | -      | -                          |
| 22.3529 | 1520 | 0.0603        | -      | -                          |
| 22.5    | 1530 | 0.0601        | -      | -                          |
| 22.6471 | 1540 | 0.0567        | -      | -                          |
| 22.7941 | 1550 | 0.0551        | 0.0911 | 0.8114                     |
| 22.9412 | 1560 | 0.0542        | -      | -                          |
| 23.0882 | 1570 | 0.057         | -      | -                          |
| 23.2353 | 1580 | 0.0541        | -      | -                          |
| 23.3824 | 1590 | 0.0586        | -      | -                          |
| 23.5294 | 1600 | 0.0573        | 0.0912 | 0.8106                     |
| 23.6765 | 1610 | 0.0543        | -      | -                          |
| 23.8235 | 1620 | 0.0578        | -      | -                          |
| 23.9706 | 1630 | 0.0563        | -      | -                          |
| 24.1176 | 1640 | 0.0549        | -      | -                          |
| 24.2647 | 1650 | 0.0549        | 0.0909 | 0.8140                     |
| 24.4118 | 1660 | 0.056         | -      | -                          |
| 24.5588 | 1670 | 0.0599        | -      | -                          |
| 24.7059 | 1680 | 0.0543        | -      | -                          |
| 24.8529 | 1690 | 0.0547        | -      | -                          |
| 25.0    | 1700 | 0.0575        | 0.0906 | 0.8114                     |
| 25.1471 | 1710 | 0.0544        | -      | -                          |
| 25.2941 | 1720 | 0.0574        | -      | -                          |
| 25.4412 | 1730 | 0.0565        | -      | -                          |
| 25.5882 | 1740 | 0.0587        | -      | -                          |
| 25.7353 | 1750 | 0.0559        | 0.0905 | 0.8157                     |
| 25.8824 | 1760 | 0.0551        | -      | -                          |
| 26.0294 | 1770 | 0.0569        | -      | -                          |
| 26.1765 | 1780 | 0.0516        | -      | -                          |
| 26.3235 | 1790 | 0.0561        | -      | -                          |
| 26.4706 | 1800 | 0.0567        | 0.0906 | 0.8165                     |
| 26.6176 | 1810 | 0.0599        | -      | -                          |
| 26.7647 | 1820 | 0.0577        | -      | -                          |
| 26.9118 | 1830 | 0.0532        | -      | -                          |
| 27.0588 | 1840 | 0.0554        | -      | -                          |
| 27.2059 | 1850 | 0.0579        | 0.0906 | 0.8123                     |
| 27.3529 | 1860 | 0.0532        | -      | -                          |
| 27.5    | 1870 | 0.0493        | -      | -                          |
| 27.6471 | 1880 | 0.0552        | -      | -                          |
| 27.7941 | 1890 | 0.0532        | -      | -                          |
| 27.9412 | 1900 | 0.0569        | 0.0904 | 0.8089                     |
| 28.0882 | 1910 | 0.0568        | -      | -                          |
| 28.2353 | 1920 | 0.052         | -      | -                          |
| 28.3824 | 1930 | 0.0555        | -      | -                          |
| 28.5294 | 1940 | 0.0563        | -      | -                          |
| 28.6765 | 1950 | 0.0555        | 0.0903 | 0.8140                     |
| 28.8235 | 1960 | 0.0535        | -      | -                          |
| 28.9706 | 1970 | 0.0525        | -      | -                          |
| 29.1176 | 1980 | 0.0566        | -      | -                          |
| 29.2647 | 1990 | 0.0562        | -      | -                          |
| 29.4118 | 2000 | 0.0547        | 0.0902 | 0.8140                     |
| 29.5588 | 2010 | 0.0495        | -      | -                          |
| 29.7059 | 2020 | 0.0532        | -      | -                          |
| 29.8529 | 2030 | 0.0553        | -      | -                          |
| 30.0    | 2040 | 0.0544        | -      | -                          |

</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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->