sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:557850 loss:StarbucksLoss en dataset:sentence-transformers/all-nli arxiv:1908.10084 base_model:ielabgroup/bert-base-uncased-fineweb100bt-smae base_model:finetune:ielabgroup/bert-base-uncased-fineweb100bt-smae model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model:
- ielabgroup/bert-base-uncased-fineweb100bt-smae
datasets:
- sentence-transformers/all-nli
language:
- en
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:557850
- loss:StarbucksLoss
widget:
- source_sentence: A dog is in the water.
  sentences:
  - The woman is wearing green.
  - The dog is rolling around in the grass.
  - A brown dog swims through water outdoors with a tennis ball in its mouth.
- source_sentence: A dog is swimming.
  sentences:
  - a black dog swimming in the water with a tennis ball in his mouth
  - A dog with yellow fur swims, neck deep, in water.
  - A brown dog running through a large orange tube.
- source_sentence: A dog is swimming.
  sentences:
  - A dog with golden hair swims through water.
  - A golden haired dog is lying in a boat that is traveling on a lake.
  - A dog with golden hair swims through water.
- source_sentence: A dog is swimming.
  sentences:
  - A tan dog splashes as he swims through the water.
  - A man and young boy asleep in a chair.
  - A dog in a harness chasing a red ball.
- source_sentence: A dog is in the water.
  sentences:
  - A big brown dog jumps into a swimming pool on the backyard.
  - Wet brown dog swims towards camera.
  - The dog is rolling around in the grass.
model-index:
- name: SentenceTransformer based on ielabgroup/bert-base-uncased-fineweb100bt-matryoshka-mae
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test
      type: sts-test
    metrics:
    - type: pearson_cosine
      value: 0.8170317205826663
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.827406310000667
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8085162876731988
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8050045835065848
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8122787407180172
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.809299222491485
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7657571947414553
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7564706925314776
      name: Spearman Dot
    - type: pearson_max
      value: 0.8170317205826663
      name: Pearson Max
    - type: spearman_max
      value: 0.827406310000667
      name: Spearman Max
---

# SentenceTransformer based on ielabgroup/bert-base-uncased-fineweb100bt-matryoshka-mae

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [ielabgroup/bert-base-uncased-fineweb100bt-matryoshka-mae](https://huggingface.co/ielabgroup/bert-base-uncased-fineweb100bt-matryoshka-mae) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. 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:** [ielabgroup/bert-base-uncased-fineweb100bt-matryoshka-mae](https://huggingface.co/ielabgroup/bert-base-uncased-fineweb100bt-matryoshka-mae) <!-- at revision 5ad87b09309fdc0a114357f37b45c4de7e4dcec6 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
- **Language:** en
<!-- - **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': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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("ielabgroup/Starbucks_STS")
# Run inference
sentences = [
    'A dog is in the water.',
    'Wet brown dog swims towards camera.',
    'The dog is rolling around in the grass.',
]
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: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.817      |
| **spearman_cosine** | **0.8274** |
| pearson_manhattan   | 0.8085     |
| spearman_manhattan  | 0.805      |
| pearson_euclidean   | 0.8123     |
| spearman_euclidean  | 0.8093     |
| pearson_dot         | 0.7658     |
| spearman_dot        | 0.7565     |
| pearson_max         | 0.817      |
| spearman_max        | 0.8274     |

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## Training Details

### Training Dataset

#### all-nli

* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 557,850 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                         |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                           |
  | details | <ul><li>min: 7 tokens</li><li>mean: 10.46 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.81 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 tokens</li></ul> |
* Samples:
  | anchor                                                                     | positive                                         | negative                                                   |
  |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------|
  | <code>A person on a horse jumps over a broken down airplane.</code>        | <code>A person is outdoors, on a horse.</code>   | <code>A person is at a diner, ordering an omelette.</code> |
  | <code>Children smiling and waving at camera</code>                         | <code>There are children present</code>          | <code>The kids are frowning</code>                         |
  | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</code>             |
* Loss: <code>starbucks_loss.StarbucksLoss</code> with these parameters:
  ```json
  {
      "loss": "MatryoshkaLoss",
      "n_selections_per_step": -1,
      "last_layer_weight": 1.0,
      "prior_layers_weight": 1.0,
      "kl_div_weight": 1.0,
      "kl_temperature": 0.3,
      "matryoshka_layers": [
          1,
          3,
          5,
          7,
          9,
          11
      ],
      "matryoshka_dims": [
          32,
          64,
          128,
          256,
          512,
          768
      ]
  }
  ```

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

- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `gradient_checkpointing`: True

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `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-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`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `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`: False
- `fp16`: True
- `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`: 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`: 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`: True
- `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 | sts-test_spearman_cosine |
|:------:|:----:|:-------------:|:------------------------:|
| 0.0229 | 100  | 16.7727       | -                        |
| 0.0459 | 200  | 9.653         | -                        |
| 0.0688 | 300  | 8.3187        | -                        |
| 0.0918 | 400  | 7.748         | -                        |
| 0.1147 | 500  | 7.2587        | -                        |
| 0.1376 | 600  | 6.734         | -                        |
| 0.1606 | 700  | 6.4463        | -                        |
| 0.1835 | 800  | 6.299         | -                        |
| 0.2065 | 900  | 5.9946        | -                        |
| 0.2294 | 1000 | 5.9348        | -                        |
| 0.2524 | 1100 | 5.7723        | -                        |
| 0.2753 | 1200 | 5.5822        | -                        |
| 0.2982 | 1300 | 5.4233        | -                        |
| 0.3212 | 1400 | 5.3427        | -                        |
| 0.3441 | 1500 | 5.3132        | -                        |
| 0.3671 | 1600 | 5.3149        | -                        |
| 0.3900 | 1700 | 5.3007        | -                        |
| 0.4129 | 1800 | 4.9539        | -                        |
| 0.4359 | 1900 | 4.9308        | -                        |
| 0.4588 | 2000 | 4.8171        | -                        |
| 0.4818 | 2100 | 5.0181        | -                        |
| 0.5047 | 2200 | 4.9631        | -                        |
| 0.5276 | 2300 | 4.8125        | -                        |
| 0.5506 | 2400 | 4.7133        | -                        |
| 0.5735 | 2500 | 4.5809        | -                        |
| 0.5965 | 2600 | 4.6093        | -                        |
| 0.6194 | 2700 | 4.6723        | -                        |
| 0.6423 | 2800 | 4.5526        | -                        |
| 0.6653 | 2900 | 4.4967        | -                        |
| 0.6882 | 3000 | 4.4178        | -                        |
| 0.7112 | 3100 | 4.4333        | -                        |
| 0.7341 | 3200 | 4.3289        | -                        |
| 0.7571 | 3300 | 4.5199        | -                        |
| 0.7800 | 3400 | 4.3389        | -                        |
| 0.8029 | 3500 | 4.3394        | -                        |
| 0.8259 | 3600 | 4.2423        | -                        |
| 0.8488 | 3700 | 4.3219        | -                        |
| 0.8718 | 3800 | 4.3297        | -                        |
| 0.8947 | 3900 | 4.3132        | -                        |
| 0.9176 | 4000 | 4.2616        | -                        |
| 0.9406 | 4100 | 4.2233        | -                        |
| 0.9635 | 4200 | 4.1912        | -                        |
| 0.9865 | 4300 | 4.1838        | -                        |
| 1.0    | 4359 | -             | 0.8274                   |


### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.1.1
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 0.33.0
- Datasets: 2.21.0
- 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",
}
```

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