---
language:
- en
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:100K<n<1M
- loss:MSELoss
base_model: w601sxs/b1ade-embed
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
- negative_mse
widget:
- source_sentence: A man is jumping.
  sentences:
  - The man is jumping off something.
  - Two people are posing for a photograph.
  - two women sing opera
- source_sentence: The wave is huge.
  sentences:
  - A person is surfing on a large wave.
  - People are competing in figure skating.
  - Cats are sleeping inside the room.
- source_sentence: The man is short.
  sentences:
  - There is a man vaucuming
  - The man did a self portrait of himself.
  - The boys are asleep in their beds.
- source_sentence: A boy is bowling.
  sentences:
  - A boy is rolling a ball in a hotel hallway.
  - PHS enrolls approximately 750 students.
  - The older men are talking about their wives.
- source_sentence: A man is walking
  sentences:
  - The man is going for a walk.
  - The station opened on 1 December 1896.
  - The woman is alone and asleep in the car on the moon.
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on w601sxs/b1ade-embed
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev
      type: sts-dev
    metrics:
    - type: pearson_cosine
      value: 0.6737565660591995
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7346594963661589
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.700631080294873
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7089388326911368
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7016605503100202
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7101559719602629
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7336031520397918
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7509506568007358
      name: Spearman Dot
    - type: pearson_max
      value: 0.7336031520397918
      name: Pearson Max
    - type: spearman_max
      value: 0.7509506568007358
      name: Spearman Max
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: negative_mse
      value: -21.545076370239258
      name: Negative Mse
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test
      type: sts-test
    metrics:
    - type: pearson_cosine
      value: 0.677225151823628
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7310810412009605
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7076654744568199
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7120808159972457
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7070890827522099
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7115055158750536
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7026111016442886
      name: Pearson Dot
    - type: spearman_dot
      value: 0.6949199269988278
      name: Spearman Dot
    - type: pearson_max
      value: 0.7076654744568199
      name: Pearson Max
    - type: spearman_max
      value: 0.7310810412009605
      name: Spearman Max
---

# SentenceTransformer based on w601sxs/b1ade-embed

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [w601sxs/b1ade-embed](https://huggingface.co/w601sxs/b1ade-embed) on the [sentence-transformers/wikipedia-en-sentences](https://huggingface.co/datasets/sentence-transformers/wikipedia-en-sentences) dataset. 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:** [w601sxs/b1ade-embed](https://huggingface.co/w601sxs/b1ade-embed) <!-- at revision fbe0925144487193887d384372a3e99bdf043596 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [sentence-transformers/wikipedia-en-sentences](https://huggingface.co/datasets/sentence-transformers/wikipedia-en-sentences)
- **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': 1024, '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("w601sxs/b1ade-embed-distilled-from-gte-large-en-v1.5")
# Run inference
sentences = [
    'A man is walking',
    'The man is going for a walk.',
    'The station opened on 1 December 1896.',
]
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

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.6738     |
| **spearman_cosine** | **0.7347** |
| pearson_manhattan   | 0.7006     |
| spearman_manhattan  | 0.7089     |
| pearson_euclidean   | 0.7017     |
| spearman_euclidean  | 0.7102     |
| pearson_dot         | 0.7336     |
| spearman_dot        | 0.751      |
| pearson_max         | 0.7336     |
| spearman_max        | 0.751      |

#### Knowledge Distillation

* Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)

| Metric           | Value        |
|:-----------------|:-------------|
| **negative_mse** | **-21.5451** |

#### 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      | 0.6772     |
| **spearman_cosine** | **0.7311** |
| pearson_manhattan   | 0.7077     |
| spearman_manhattan  | 0.7121     |
| pearson_euclidean   | 0.7071     |
| spearman_euclidean  | 0.7115     |
| pearson_dot         | 0.7026     |
| spearman_dot        | 0.6949     |
| pearson_max         | 0.7077     |
| spearman_max        | 0.7311     |

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## Training Details

### Training Dataset

#### sentence-transformers/wikipedia-en-sentences

* Dataset: [sentence-transformers/wikipedia-en-sentences](https://huggingface.co/datasets/sentence-transformers/wikipedia-en-sentences) at [4a0972d](https://huggingface.co/datasets/sentence-transformers/wikipedia-en-sentences/tree/4a0972dcb781b5b5d27799798f032606421dd422)
* Size: 200,000 training samples
* Columns: <code>sentence</code> and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence                                                                          | label                                 |
  |:--------|:----------------------------------------------------------------------------------|:--------------------------------------|
  | type    | string                                                                            | list                                  |
  | details | <ul><li>min: 4 tokens</li><li>mean: 12.24 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>size: 1024 elements</li></ul> |
* Samples:
  | sentence                                                                   | label                                                                                                                       |
  |:---------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------|
  | <code>A person on a horse jumps over a broken down airplane.</code>        | <code>[-0.5300068259239197, 0.07807248830795288, 0.304331511259079, 0.3473575711250305, 0.3993019461631775, ...]</code>     |
  | <code>Children smiling and waving at camera</code>                         | <code>[-0.3918086886405945, 0.514893114566803, 0.38178062438964844, -0.29475438594818115, -0.07984668761491776, ...]</code> |
  | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>[-0.7029106020927429, 0.08336036652326584, 0.7830113768577576, -0.7898964285850525, 0.27573251724243164, ...]</code>  |
* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)

### Evaluation Dataset

#### sentence-transformers/wikipedia-en-sentences

* Dataset: [sentence-transformers/wikipedia-en-sentences](https://huggingface.co/datasets/sentence-transformers/wikipedia-en-sentences) at [4a0972d](https://huggingface.co/datasets/sentence-transformers/wikipedia-en-sentences/tree/4a0972dcb781b5b5d27799798f032606421dd422)
* Size: 10,000 evaluation samples
* Columns: <code>sentence</code> and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence                                                                          | label                                 |
  |:--------|:----------------------------------------------------------------------------------|:--------------------------------------|
  | type    | string                                                                            | list                                  |
  | details | <ul><li>min: 5 tokens</li><li>mean: 13.23 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>size: 1024 elements</li></ul> |
* Samples:
  | sentence                                                                                                                                                                       | label                                                                                                                       |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------|
  | <code>Two women are embracing while holding to go packages.</code>                                                                                                             | <code>[-0.5707114338874817, -0.5041555762290955, -1.3100334405899048, 0.5848354697227478, -0.3452240526676178, ...]</code>  |
  | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>[-0.4810343384742737, 0.034435614943504333, -0.669406533241272, -0.16233624517917633, 0.5214978456497192, ...]</code> |
  | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code>                                                                    | <code>[-0.2572114169597626, 0.19592943787574768, -0.6243088841438293, -0.4749126136302948, -0.6737443804740906, ...]</code> |
* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 0.0001
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: 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`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 0.0001
- `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`: 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
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step    | Training Loss | loss       | negative_mse | sts-dev_spearman_cosine | sts-test_spearman_cosine |
|:----------:|:-------:|:-------------:|:----------:|:------------:|:-----------------------:|:------------------------:|
| 0.1279     | 100     | 0.4302        | -          | -            | -                       | -                        |
| 0.2558     | 200     | 0.2398        | -          | -            | -                       | -                        |
| 0.3836     | 300     | 0.1918        | -          | -            | -                       | -                        |
| 0.5115     | 400     | 0.1683        | -          | -            | -                       | -                        |
| **0.6394** | **500** | **0.1539**    | **0.2155** | **-21.5451** | **0.7347**              | **-**                    |
| 0.7673     | 600     | 0.1456        | -          | -            | -                       | -                        |
| 0.8951     | 700     | 0.1393        | -          | -            | -                       | -                        |
| 1.0        | 782     | -             | -          | -            | -                       | 0.7311                   |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.6
- Sentence Transformers: 3.0.0
- Transformers: 4.41.1
- PyTorch: 2.3.0+cu121
- Accelerate: 0.30.1
- 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",
}
```

#### MSELoss
```bibtex
@inproceedings{reimers-2020-multilingual-sentence-bert,
    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2020",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/2004.09813",
}
```

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