README.md

---
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
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: beatai dev
      type: beatai-dev
    metrics:
    - type: cosine_accuracy
      value: 0.8080808080808081
      name: Cosine Accuracy
    - type: dot_accuracy
      value: 0.28114478114478114
      name: Dot Accuracy
    - type: manhattan_accuracy
      value: 0.8316498316498316
      name: Manhattan Accuracy
    - type: euclidean_accuracy
      value: 0.8249158249158249
      name: Euclidean Accuracy
    - type: max_accuracy
      value: 0.8316498316498316
      name: Max Accuracy
---

# SentenceTransformer

This is a [sentence-transformers](https://www.SBERT.net) model trained. 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.

1. bert-base-uncased was pretrained on a large corpus of open access philosophy text.
2. This model was further trained using TSDAE on a subset of sentences from this corpus for 6 epochs. 
3. Resulting model was finetuned using cosine similarity objective on the "philsim" private dataset.
4. Resulting model was finetuned using cosine similarity objective on the beatai-philosophy dataset.

Model internal name: pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-20e

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **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/philai-embeddings-2.0")
# 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]
```

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## 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    | 0.8081     |
| dot_accuracy       | 0.2811     |
| manhattan_accuracy | 0.8316     |
| euclidean_accuracy | 0.8249     |
| **max_accuracy**   | **0.8316** |

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## Training Details

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 138
- `per_device_eval_batch_size`: 138
- `learning_rate`: 2e-06
- `num_train_epochs`: 10
- `lr_scheduler_type`: constant
- `bf16`: True
- `dataloader_drop_last`: 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`: 2e-06
- `weight_decay`: 0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 10
- `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`: 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
- `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
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | loss   | beatai-dev_max_accuracy |
|:------:|:----:|:-------------:|:------:|:-----------------------:|
| 0      | 0    | -             | -      | 0.8072                  |
| 0.1471 | 10   | 1.8573        | -      | -                       |
| 0.2941 | 20   | 1.8196        | -      | -                       |
| 0.4412 | 30   | 1.8594        | -      | -                       |
| 0.5882 | 40   | 1.8581        | -      | -                       |
| 0.7353 | 50   | 1.8766        | 2.3603 | 0.8047                  |
| 0.8824 | 60   | 1.8596        | -      | -                       |
| 1.0294 | 70   | 1.6816        | -      | -                       |
| 1.1765 | 80   | 1.7564        | -      | -                       |
| 1.3235 | 90   | 1.7191        | -      | -                       |
| 1.4706 | 100  | 1.6521        | 2.3296 | 0.8064                  |
| 1.6176 | 110  | 1.7054        | -      | -                       |
| 1.7647 | 120  | 1.6895        | -      | -                       |
| 1.9118 | 130  | 1.6724        | -      | -                       |
| 2.0588 | 140  | 1.6369        | -      | -                       |
| 2.2059 | 150  | 1.705         | 2.2941 | 0.8123                  |
| 2.3529 | 160  | 1.8329        | -      | -                       |
| 2.5    | 170  | 1.6071        | -      | -                       |
| 2.6471 | 180  | 1.5157        | -      | -                       |
| 2.7941 | 190  | 1.624         | -      | -                       |
| 2.9412 | 200  | 1.6185        | 2.2668 | 0.8140                  |
| 3.0882 | 210  | 1.6259        | -      | -                       |
| 3.2353 | 220  | 1.5749        | -      | -                       |
| 3.3824 | 230  | 1.5426        | -      | -                       |
| 3.5294 | 240  | 1.5522        | -      | -                       |
| 3.6765 | 250  | 1.5141        | 2.2498 | 0.8157                  |
| 3.8235 | 260  | 1.5215        | -      | -                       |
| 3.9706 | 270  | 1.4983        | -      | -                       |
| 4.1176 | 280  | 1.4819        | -      | -                       |
| 4.2647 | 290  | 1.4552        | -      | -                       |
| 4.4118 | 300  | 1.5597        | 2.2226 | 0.8199                  |
| 4.5588 | 310  | 1.3983        | -      | -                       |
| 4.7059 | 320  | 1.5386        | -      | -                       |
| 4.8529 | 330  | 1.4541        | -      | -                       |
| 5.0    | 340  | 1.4097        | -      | -                       |
| 5.1471 | 350  | 1.3741        | 2.2129 | 0.8207                  |
| 5.2941 | 360  | 1.3909        | -      | -                       |
| 5.4412 | 370  | 1.4116        | -      | -                       |
| 5.5882 | 380  | 1.52          | -      | -                       |
| 5.7353 | 390  | 1.3644        | -      | -                       |
| 5.8824 | 400  | 1.3016        | 2.1699 | 0.8266                  |
| 6.0294 | 410  | 1.4435        | -      | -                       |
| 6.1765 | 420  | 1.3112        | -      | -                       |
| 6.3235 | 430  | 1.4056        | -      | -                       |
| 6.4706 | 440  | 1.4541        | -      | -                       |
| 6.6176 | 450  | 1.3312        | 2.1486 | 0.8224                  |
| 6.7647 | 460  | 1.2879        | -      | -                       |
| 6.9118 | 470  | 1.227         | -      | -                       |
| 7.0588 | 480  | 1.3834        | -      | -                       |
| 7.2059 | 490  | 1.3242        | -      | -                       |
| 7.3529 | 500  | 1.3756        | 2.1507 | 0.8274                  |
| 7.5    | 510  | 1.2872        | -      | -                       |
| 7.6471 | 520  | 1.3288        | -      | -                       |
| 7.7941 | 530  | 1.2689        | -      | -                       |
| 7.9412 | 540  | 1.3102        | -      | -                       |
| 8.0882 | 550  | 1.2929        | 2.1355 | 0.8207                  |
| 8.2353 | 560  | 1.2511        | -      | -                       |
| 8.3824 | 570  | 1.1849        | -      | -                       |
| 8.5294 | 580  | 1.2774        | -      | -                       |
| 8.6765 | 590  | 1.1923        | -      | -                       |
| 8.8235 | 600  | 1.1927        | 2.1111 | 0.8283                  |
| 8.9706 | 610  | 1.2556        | -      | -                       |
| 9.1176 | 620  | 1.2767        | -      | -                       |
| 9.2647 | 630  | 1.1082        | -      | -                       |
| 9.4118 | 640  | 1.3077        | -      | -                       |
| 9.5588 | 650  | 1.1435        | 2.0922 | 0.8316                  |
| 9.7059 | 660  | 1.1888        | -      | -                       |
| 9.8529 | 670  | 1.2123        | -      | -                       |
| 10.0   | 680  | 1.2554        | -      | -                       |


### Framework Versions
- Python: 3.8.18
- Sentence Transformers: 3.1.1
- Transformers: 4.44.2
- PyTorch: 1.13.1+cu117
- Accelerate: 0.34.2
- Datasets: 3.0.0
- Tokenizers: 0.19.1

## 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",
}
```

#### 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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