---
base_model: dbourget/philai-embeddings-2.0
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/philai-embeddings-2.0
results:
- task:
type: triplet
name: Triplet
dataset:
name: beatai dev
type: beatai-dev
metrics:
- type: cosine_accuracy
value: 0.8215488215488216
name: Cosine Accuracy
- type: dot_accuracy
value: 0.24494949494949494
name: Dot Accuracy
- type: manhattan_accuracy
value: 0.835016835016835
name: Manhattan Accuracy
- type: euclidean_accuracy
value: 0.8341750841750841
name: Euclidean Accuracy
- type: max_accuracy
value: 0.835016835016835
name: Max Accuracy
---
# SentenceTransformer based on dbourget/philai-embeddings-2.0
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [dbourget/philai-embeddings-2.0](https://huggingface.co/dbourget/philai-embeddings-2.0). 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/philai-embeddings-2.0](https://huggingface.co/dbourget/philai-embeddings-2.0)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
### 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-30e")
# 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]
```
## Evaluation
### Metrics
#### Triplet
* Dataset: `beatai-dev`
* Evaluated with [TripletEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:-------------------|:----------|
| cosine_accuracy | 0.8215 |
| dot_accuracy | 0.2449 |
| manhattan_accuracy | 0.835 |
| euclidean_accuracy | 0.8342 |
| **max_accuracy** | **0.835** |
## Training Details
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 138
- `per_device_eval_batch_size`: 138
- `learning_rate`: 1e-06
- `weight_decay`: 0.01
- `num_train_epochs`: 20
- `lr_scheduler_type`: constant
- `bf16`: True
- `dataloader_drop_last`: True
- `resume_from_checkpoint`: True
#### All Hyperparameters
Click to expand
- `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`: 1e-06
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 20
- `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
### Training Logs
Click to expand
| Epoch | Step | Training Loss | loss | beatai-dev_max_accuracy |
|:-------:|:----:|:-------------:|:------:|:-----------------------:|
| 0 | 0 | - | - | 0.8308 |
| 0.1471 | 10 | 1.056 | - | - |
| 0.2941 | 20 | 1.0992 | - | - |
| 0.4412 | 30 | 1.1678 | - | - |
| 0.5882 | 40 | 1.1586 | - | - |
| 0.7353 | 50 | 1.1777 | 2.0793 | 0.8291 |
| 0.8824 | 60 | 1.1344 | - | - |
| 1.0294 | 70 | 1.0578 | - | - |
| 1.1765 | 80 | 1.0981 | - | - |
| 1.3235 | 90 | 1.1216 | - | - |
| 1.4706 | 100 | 1.0436 | 2.0826 | 0.8283 |
| 1.6176 | 110 | 1.0422 | - | - |
| 1.7647 | 120 | 1.0857 | - | - |
| 1.9118 | 130 | 1.0502 | - | - |
| 2.0588 | 140 | 1.0363 | - | - |
| 2.2059 | 150 | 1.081 | 2.0763 | 0.8316 |
| 2.3529 | 160 | 1.1764 | - | - |
| 2.5 | 170 | 1.0393 | - | - |
| 2.6471 | 180 | 0.9586 | - | - |
| 2.7941 | 190 | 1.0537 | - | - |
| 2.9412 | 200 | 1.0313 | 2.0645 | 0.8325 |
| 3.0882 | 210 | 1.0401 | - | - |
| 3.2353 | 220 | 1.0389 | - | - |
| 3.3824 | 230 | 1.0225 | - | - |
| 3.5294 | 240 | 1.0131 | - | - |
| 3.6765 | 250 | 0.9565 | 2.0705 | 0.8308 |
| 3.8235 | 260 | 1.0059 | - | - |
| 3.9706 | 270 | 0.9629 | - | - |
| 4.1176 | 280 | 0.9546 | - | - |
| 4.2647 | 290 | 0.989 | - | - |
| 4.4118 | 300 | 1.0573 | 2.0514 | 0.8375 |
| 4.5588 | 310 | 0.894 | - | - |
| 4.7059 | 320 | 1.0082 | - | - |
| 4.8529 | 330 | 0.969 | - | - |
| 5.0 | 340 | 0.9187 | - | - |
| 5.1471 | 350 | 0.9034 | 2.0663 | 0.8350 |
| 5.2941 | 360 | 0.9043 | - | - |
| 5.4412 | 370 | 0.9517 | - | - |
| 5.5882 | 380 | 1.0272 | - | - |
| 5.7353 | 390 | 0.95 | - | - |
| 5.8824 | 400 | 0.8288 | 2.0400 | 0.8367 |
| 6.0294 | 410 | 0.9809 | - | - |
| 6.1765 | 420 | 0.8776 | - | - |
| 6.3235 | 430 | 0.9744 | - | - |
| 6.4706 | 440 | 0.9982 | - | - |
| 6.6176 | 450 | 0.9076 | 2.0429 | 0.8350 |
| 6.7647 | 460 | 0.8792 | - | - |
| 6.9118 | 470 | 0.787 | - | - |
| 7.0588 | 480 | 0.9506 | - | - |
| 7.2059 | 490 | 0.927 | - | - |
| 7.3529 | 500 | 0.9464 | 2.0487 | 0.8316 |
| 7.5 | 510 | 0.886 | - | - |
| 7.6471 | 520 | 0.9142 | - | - |
| 7.7941 | 530 | 0.8741 | - | - |
| 7.9412 | 540 | 0.8703 | - | - |
| 8.0882 | 550 | 0.8947 | 2.0411 | 0.8333 |
| 8.2353 | 560 | 0.8742 | - | - |
| 8.3824 | 570 | 0.8083 | - | - |
| 8.5294 | 580 | 0.9134 | - | - |
| 8.6765 | 590 | 0.8197 | - | - |
| 8.8235 | 600 | 0.8253 | 2.0272 | 0.8367 |
| 8.9706 | 610 | 0.8665 | - | - |
| 9.1176 | 620 | 0.8853 | - | - |
| 9.2647 | 630 | 0.7566 | - | - |
| 9.4118 | 640 | 0.9101 | - | - |
| 9.5588 | 650 | 0.801 | 2.0243 | 0.8350 |
| 9.7059 | 660 | 0.8551 | - | - |
| 9.8529 | 670 | 0.8748 | - | - |
| 10.0 | 680 | 0.9798 | - | - |
| 10.1471 | 690 | 1.0544 | - | - |
| 10.2941 | 700 | 1.2077 | 2.0128 | 0.8367 |
| 10.4412 | 710 | 1.0386 | - | - |
| 10.5882 | 720 | 1.0508 | - | - |
| 10.7353 | 730 | 1.0063 | - | - |
| 10.8824 | 740 | 1.0758 | - | - |
| 11.0294 | 750 | 1.1552 | 2.0031 | 0.8367 |
| 11.1765 | 760 | 1.0259 | - | - |
| 11.3235 | 770 | 1.0724 | - | - |
| 11.4706 | 780 | 1.0524 | - | - |
| 11.6176 | 790 | 0.9957 | - | - |
| 11.7647 | 800 | 1.0697 | 2.0022 | 0.8367 |
| 11.9118 | 810 | 1.0544 | - | - |
| 12.0588 | 820 | 1.0762 | - | - |
| 12.2059 | 830 | 1.0858 | - | - |
| 12.3529 | 840 | 1.0418 | - | - |
| 12.5 | 850 | 1.0041 | 1.9936 | 0.8392 |
| 12.6471 | 860 | 0.998 | - | - |
| 12.7941 | 870 | 1.0737 | - | - |
| 12.9412 | 880 | 1.0637 | - | - |
| 13.0882 | 890 | 0.9689 | - | - |
| 13.2353 | 900 | 1.001 | 1.9818 | 0.8392 |
| 13.3824 | 910 | 1.0418 | - | - |
| 13.5294 | 920 | 1.0097 | - | - |
| 13.6765 | 930 | 1.0244 | - | - |
| 13.8235 | 940 | 1.0383 | - | - |
| 13.9706 | 950 | 1.034 | 1.9798 | 0.8367 |
| 14.1176 | 960 | 0.9609 | - | - |
| 14.2647 | 970 | 1.049 | - | - |
| 14.4118 | 980 | 1.0012 | - | - |
| 14.5588 | 990 | 0.9008 | - | - |
| 14.7059 | 1000 | 1.0131 | 1.9741 | 0.8384 |
| 14.8529 | 1010 | 0.9714 | - | - |
| 15.0 | 1020 | 0.9987 | - | - |
| 15.1471 | 1030 | 1.1139 | - | - |
| 15.2941 | 1040 | 1.005 | - | - |
| 15.4412 | 1050 | 0.9074 | 1.9761 | 0.8359 |
| 15.5882 | 1060 | 0.9298 | - | - |
| 15.7353 | 1070 | 0.9335 | - | - |
| 15.8824 | 1080 | 0.9445 | - | - |
| 16.0294 | 1090 | 1.0087 | - | - |
| 16.1765 | 1100 | 0.9187 | 1.9679 | 0.8384 |
| 16.3235 | 1110 | 0.8502 | - | - |
| 16.4706 | 1120 | 0.9924 | - | - |
| 16.6176 | 1130 | 0.9982 | - | - |
| 16.7647 | 1140 | 0.9643 | - | - |
| 16.9118 | 1150 | 0.9491 | 1.9727 | 0.8333 |
| 17.0588 | 1160 | 0.9801 | - | - |
| 17.2059 | 1170 | 0.9374 | - | - |
| 17.3529 | 1180 | 0.8309 | - | - |
| 17.5 | 1190 | 0.9524 | - | - |
| 17.6471 | 1200 | 0.886 | 1.9797 | 0.8350 |
| 17.7941 | 1210 | 0.9026 | - | - |
| 17.9412 | 1220 | 0.8859 | - | - |
| 18.0882 | 1230 | 0.8745 | - | - |
| 18.2353 | 1240 | 0.9474 | - | - |
| 18.3824 | 1250 | 0.878 | 1.9737 | 0.8342 |
| 18.5294 | 1260 | 0.8372 | - | - |
| 18.6765 | 1270 | 0.833 | - | - |
| 18.8235 | 1280 | 0.9648 | - | - |
| 18.9706 | 1290 | 0.918 | - | - |
| 19.1176 | 1300 | 0.9588 | 1.9669 | 0.8359 |
| 19.2647 | 1310 | 1.0334 | - | - |
| 19.4118 | 1320 | 0.8347 | - | - |
| 19.5588 | 1330 | 0.828 | - | - |
| 19.7059 | 1340 | 0.9117 | - | - |
| 19.8529 | 1350 | 0.9123 | 1.9666 | 0.8350 |
| 20.0 | 1360 | 0.8538 | - | - |
### Framework Versions
- Python: 3.8.18
- Sentence Transformers: 3.1.1
- Transformers: 4.45.0
- PyTorch: 1.13.1+cu117
- Accelerate: 0.34.2
- Datasets: 3.0.1
- 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}
}
```