---
language:
- en
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1625
- loss:CosineSimilarityLoss
base_model: BAAI/bge-base-en-v1.5
datasets: []
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: Boron Steel
  sentences:
  - Rock Bit
  - Spalling Test
  - Excavator Bucket
- source_sentence: Friction Wear
  sentences:
  - Tool Steel
  - Medium Carbon Steel
  - Diffusion Bonding
- source_sentence: Delamination
  sentences:
  - Subsea Christmas Tree
  - Low Alloyed Steel
  - Screw Conveyors
- source_sentence: Nitriding
  sentences:
  - Subsea Manifold
  - Trencher Chain
  - Cylinder
- source_sentence: Corrosion Resistant Coatings
  sentences:
  - Mower Blade
  - Gas Metal Arc Welding (GMAW)
  - Corrosion Resistant Coatings
pipeline_tag: sentence-similarity
model-index:
- name: BGE base Financial Matryoshka
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: pearson_cosine
      value: 0.9548051644723275
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6620048542679903
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.985909077336812
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6620048542679903
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.9863519709955113
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6620048542679903
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.9548051701614557
      name: Pearson Dot
    - type: spearman_dot
      value: 0.6610658947764548
      name: Spearman Dot
    - type: pearson_max
      value: 0.9863519709955113
      name: Pearson Max
    - type: spearman_max
      value: 0.6620048542679903
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: pearson_cosine
      value: 0.9544417196413574
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6620048542679903
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.9855825558550574
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6620048542679903
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.9862004412296757
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6620048542679903
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.9501184326722917
      name: Pearson Dot
    - type: spearman_dot
      value: 0.6607798700248341
      name: Spearman Dot
    - type: pearson_max
      value: 0.9862004412296757
      name: Pearson Max
    - type: spearman_max
      value: 0.6620048542679903
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: pearson_cosine
      value: 0.9494511778471465
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6620048542679903
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.9830259644213172
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6620048542679903
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.9835562939431381
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6620048542679903
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.9469313992827345
      name: Pearson Dot
    - type: spearman_dot
      value: 0.6607798700248341
      name: Spearman Dot
    - type: pearson_max
      value: 0.9835562939431381
      name: Pearson Max
    - type: spearman_max
      value: 0.6620048542679903
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: pearson_cosine
      value: 0.9397052405386266
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6620048542679903
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.9762184586055923
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6620048542679903
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.9781975526221939
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6620048542679903
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.9271211389022183
      name: Pearson Dot
    - type: spearman_dot
      value: 0.6607798700248341
      name: Spearman Dot
    - type: pearson_max
      value: 0.9781975526221939
      name: Pearson Max
    - type: spearman_max
      value: 0.6620048542679903
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: pearson_cosine
      value: 0.9149032642312528
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6620048542679903
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.968215524939354
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6620048542679903
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.9708485057392984
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6620048542679903
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8940456314300972
      name: Pearson Dot
    - type: spearman_dot
      value: 0.6602255244962898
      name: Spearman Dot
    - type: pearson_max
      value: 0.9708485057392984
      name: Pearson Max
    - type: spearman_max
      value: 0.6620048542679903
      name: Spearman Max
---

# BGE base Financial Matryoshka

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0

### 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': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, '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})
  (2): Normalize()
)
```

## 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("thetayne/finetuned_model_0613")
# Run inference
sentences = [
    'Corrosion Resistant Coatings',
    'Corrosion Resistant Coatings',
    'Mower Blade',
]
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]
```

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## Evaluation

### Metrics

#### Semantic Similarity
* Dataset: `dim_768`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value     |
|:--------------------|:----------|
| pearson_cosine      | 0.9548    |
| **spearman_cosine** | **0.662** |
| pearson_manhattan   | 0.9859    |
| spearman_manhattan  | 0.662     |
| pearson_euclidean   | 0.9864    |
| spearman_euclidean  | 0.662     |
| pearson_dot         | 0.9548    |
| spearman_dot        | 0.6611    |
| pearson_max         | 0.9864    |
| spearman_max        | 0.662     |

#### Semantic Similarity
* Dataset: `dim_512`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value     |
|:--------------------|:----------|
| pearson_cosine      | 0.9544    |
| **spearman_cosine** | **0.662** |
| pearson_manhattan   | 0.9856    |
| spearman_manhattan  | 0.662     |
| pearson_euclidean   | 0.9862    |
| spearman_euclidean  | 0.662     |
| pearson_dot         | 0.9501    |
| spearman_dot        | 0.6608    |
| pearson_max         | 0.9862    |
| spearman_max        | 0.662     |

#### Semantic Similarity
* Dataset: `dim_256`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value     |
|:--------------------|:----------|
| pearson_cosine      | 0.9495    |
| **spearman_cosine** | **0.662** |
| pearson_manhattan   | 0.983     |
| spearman_manhattan  | 0.662     |
| pearson_euclidean   | 0.9836    |
| spearman_euclidean  | 0.662     |
| pearson_dot         | 0.9469    |
| spearman_dot        | 0.6608    |
| pearson_max         | 0.9836    |
| spearman_max        | 0.662     |

#### Semantic Similarity
* Dataset: `dim_128`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value     |
|:--------------------|:----------|
| pearson_cosine      | 0.9397    |
| **spearman_cosine** | **0.662** |
| pearson_manhattan   | 0.9762    |
| spearman_manhattan  | 0.662     |
| pearson_euclidean   | 0.9782    |
| spearman_euclidean  | 0.662     |
| pearson_dot         | 0.9271    |
| spearman_dot        | 0.6608    |
| pearson_max         | 0.9782    |
| spearman_max        | 0.662     |

#### Semantic Similarity
* Dataset: `dim_64`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value     |
|:--------------------|:----------|
| pearson_cosine      | 0.9149    |
| **spearman_cosine** | **0.662** |
| pearson_manhattan   | 0.9682    |
| spearman_manhattan  | 0.662     |
| pearson_euclidean   | 0.9708    |
| spearman_euclidean  | 0.662     |
| pearson_dot         | 0.894     |
| spearman_dot        | 0.6602    |
| pearson_max         | 0.9708    |
| spearman_max        | 0.662     |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 1,625 training samples
* Columns: <code>sentence_A</code>, <code>sentence_B</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_A                                                                       | sentence_B                                                                       | score                                           |
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                           | string                                                                           | int                                             |
  | details | <ul><li>min: 3 tokens</li><li>mean: 5.68 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.73 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>0: ~83.30%</li><li>1: ~16.70%</li></ul> |
* Samples:
  | sentence_A                         | sentence_B                            | score          |
  |:-----------------------------------|:--------------------------------------|:---------------|
  | <code>Thermal Fatigue</code>       | <code>Ferritic Stainless Steel</code> | <code>0</code> |
  | <code>High Temperature Wear</code> | <code>Drill String</code>             | <code>0</code> |
  | <code>Carbide Coatings</code>      | <code>Carbide Coatings</code>         | <code>1</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

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

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-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`: cosine
- `lr_scheduler_kwargs`: {}
- `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
- `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`: True
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `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_fused
- `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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step   | Training Loss | dim_128_spearman_cosine | dim_256_spearman_cosine | dim_512_spearman_cosine | dim_64_spearman_cosine | dim_768_spearman_cosine |
|:----------:|:------:|:-------------:|:-----------------------:|:-----------------------:|:-----------------------:|:----------------------:|:-----------------------:|
| 0          | 0      | -             | 0.6626                  | 0.6626                  | 0.6626                  | 0.6626                 | 0.6626                  |
| 0.9412     | 3      | -             | 0.6620                  | 0.6620                  | 0.6620                  | 0.6620                 | 0.6620                  |
| 1.8627     | 6      | -             | 0.6620                  | 0.6620                  | 0.6620                  | 0.6620                 | 0.6620                  |
| 2.7843     | 9      | -             | 0.6620                  | 0.6620                  | 0.6620                  | 0.6620                 | 0.6620                  |
| 3.0784     | 10     | 0.156         | -                       | -                       | -                       | -                      | -                       |
| **3.7059** | **12** | **-**         | **0.662**               | **0.662**               | **0.662**               | **0.662**              | **0.662**               |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.31.0
- Datasets: 2.19.1
- 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",
}
```

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