sentence-transformers safetensors modernbert sentence-similarity feature-extraction generated_from_trainer dataset_size:5749 loss:MatryoshkaLoss loss:CoSENTLoss en dataset:sentence-transformers/stsb arxiv:1908.10084 arxiv:2205.13147 base_model:answerdotai/ModernBERT-large base_model:finetune:answerdotai/ModernBERT-large model-index autotrain_compatible endpoints_compatible region:us
---
base_model: answerdotai/ModernBERT-large
datasets:
- sentence-transformers/stsb
language:
- en
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:5749
- loss:MatryoshkaLoss
- loss:CoSENTLoss
widget:
- source_sentence: The man talked to a girl over the internet camera.
  sentences:
  - A group of elderly people pose around a dining table.
  - A teenager talks to a girl over a webcam.
  - There is no 'still' that is not relative to some other object.
- source_sentence: A woman is writing something.
  sentences:
  - Two eagles are perched on a branch.
  - It refers to the maximum f-stop (which is defined as the ratio of focal length
    to effective aperture diameter).
  - A woman is chopping green onions.
- source_sentence: The player shoots the winning points.
  sentences:
  - Minimum wage laws hurt the least skilled, least productive the most.
  - The basketball player is about to score points for his team.
  - Sheep are grazing in the field in front of a line of trees.
- source_sentence: Stars form in star-formation regions, which itself develop from
    molecular clouds.
  sentences:
  - Although I believe Searle is mistaken, I don't think you have found the problem.
  - It may be possible for a solar system like ours to exist outside of a galaxy.
  - A blond-haired child performing on the trumpet in front of a house while his younger
    brother watches.
- source_sentence: While Queen may refer to both Queen regent (sovereign) or Queen
    consort, the King has always been the sovereign.
  sentences:
  - At first, I thought this is a bit of a tricky question.
  - A man sitting on the floor in a room is strumming a guitar.
  - There is a very good reason not to refer to the Queen's spouse as "King" - because
    they aren't the King.
model-index:
- name: SentenceTransformer based on answerdotai/ModernBERT-large
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev
      type: sts-dev
    metrics:
    - type: pearson_cosine
      value: 0.8806182367761234
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8877448358326038
      name: Spearman Cosine
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test
      type: sts-test
    metrics:
    - type: pearson_cosine
      value: 0.8505275385588008
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8678439086871484
      name: Spearman Cosine
---

# SentenceTransformer based on answerdotai/ModernBERT-large

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) on the [stsb](https://huggingface.co/datasets/sentence-transformers/stsb) 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:** [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) <!-- at revision 4bbcbf40bed02ce487125bcb3c897ea9bdc88340 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [stsb](https://huggingface.co/datasets/sentence-transformers/stsb)
- **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': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel 
  (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("nickprock/ModernBERT-large-sts")
# Run inference
sentences = [
    'While Queen may refer to both Queen regent (sovereign) or Queen consort, the King has always been the sovereign.',
    'There is a very good reason not to refer to the Queen\'s spouse as "King" - because they aren\'t the King.',
    'A man sitting on the floor in a room is strumming a guitar.',
]
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

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

| Metric              | sts-dev    | sts-test   |
|:--------------------|:-----------|:-----------|
| pearson_cosine      | 0.8806     | 0.8505     |
| **spearman_cosine** | **0.8877** | **0.8678** |

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## Training Details

### Training Dataset

#### stsb

* Dataset: [stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                         | score                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | float                                                          |
  | details | <ul><li>min: 6 tokens</li><li>mean: 10.16 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.12 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                  | sentence2                                                             | score             |
  |:-----------------------------------------------------------|:----------------------------------------------------------------------|:------------------|
  | <code>A plane is taking off.</code>                        | <code>An air plane is taking off.</code>                              | <code>1.0</code>  |
  | <code>A man is playing a large flute.</code>               | <code>A man is playing a flute.</code>                                | <code>0.76</code> |
  | <code>A man is spreading shreded cheese on a pizza.</code> | <code>A man is spreading shredded cheese on an uncooked pizza.</code> | <code>0.76</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "CoSENTLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Evaluation Dataset

#### stsb

* Dataset: [stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                        | score                                                          |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           | float                                                          |
  | details | <ul><li>min: 5 tokens</li><li>mean: 15.11 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.1 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                         | sentence2                                             | score             |
  |:--------------------------------------------------|:------------------------------------------------------|:------------------|
  | <code>A man with a hard hat is dancing.</code>    | <code>A man wearing a hard hat is dancing.</code>     | <code>1.0</code>  |
  | <code>A young child is riding a horse.</code>     | <code>A child is riding a horse.</code>               | <code>0.95</code> |
  | <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</code> | <code>1.0</code>  |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "CoSENTLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 10
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates

#### 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`: 16
- `per_device_eval_batch_size`: 16
- `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`: 10
- `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`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | Validation Loss | sts-dev_spearman_cosine | sts-test_spearman_cosine |
|:------:|:----:|:-------------:|:---------------:|:-----------------------:|:------------------------:|
| 0.2778 | 100  | 25.6058       | 22.1112         | 0.7926                  | -                        |
| 0.5556 | 200  | 21.8238       | 21.6575         | 0.8499                  | -                        |
| 0.8333 | 300  | 21.633        | 21.2353         | 0.8684                  | -                        |
| 1.1111 | 400  | 22.3829       | 21.8035         | 0.8373                  | -                        |
| 1.3889 | 500  | 22.0584       | 23.0027         | 0.8228                  | -                        |
| 1.6667 | 600  | 21.6662       | 22.3269         | 0.8545                  | -                        |
| 1.9444 | 700  | 21.2545       | 21.3335         | 0.8592                  | -                        |
| 2.2222 | 800  | 20.5104       | 21.8647         | 0.8580                  | -                        |
| 2.5    | 900  | 20.8763       | 21.8435         | 0.8631                  | -                        |
| 2.7778 | 1000 | 20.3502       | 21.9781         | 0.8682                  | -                        |
| 3.0556 | 1100 | 20.1262       | 22.3008         | 0.8662                  | -                        |
| 3.3333 | 1200 | 20.0832       | 21.4932         | 0.8733                  | -                        |
| 3.6111 | 1300 | 19.8407       | 22.9816         | 0.8661                  | -                        |
| 3.8889 | 1400 | 20.027        | 22.3290         | 0.8729                  | -                        |
| 4.1667 | 1500 | 19.2652       | 23.7340         | 0.8718                  | -                        |
| 4.4444 | 1600 | 19.5304       | 23.4634         | 0.8766                  | -                        |
| 4.7222 | 1700 | 19.6657       | 23.3991         | 0.8764                  | -                        |
| 5.0    | 1800 | 18.8885       | 24.1863         | 0.8825                  | -                        |
| 5.2778 | 1900 | 19.1028       | 23.9508         | 0.8781                  | -                        |
| 5.5556 | 2000 | 19.0076       | 23.6006         | 0.8814                  | -                        |
| 5.8333 | 2100 | 18.472        | 24.0162         | 0.8786                  | -                        |
| 6.1111 | 2200 | 18.3949       | 24.2914         | 0.8839                  | -                        |
| 6.3889 | 2300 | 17.6192       | 26.2586         | 0.8785                  | -                        |
| 6.6667 | 2400 | 18.0109       | 25.8655         | 0.8820                  | -                        |
| 6.9444 | 2500 | 17.8948       | 24.8124         | 0.8830                  | -                        |
| 7.2222 | 2600 | 17.6087       | 26.6571         | 0.8837                  | -                        |
| 7.5    | 2700 | 17.1578       | 26.9229         | 0.8838                  | -                        |
| 7.7778 | 2800 | 17.0154       | 27.1973         | 0.8850                  | -                        |
| 8.0556 | 2900 | 16.5323       | 28.2881         | 0.8836                  | -                        |
| 8.3333 | 3000 | 16.0817       | 28.4812         | 0.8874                  | -                        |
| 8.6111 | 3100 | 16.1146       | 29.0393         | 0.8869                  | -                        |
| 8.8889 | 3200 | 16.0888       | 29.6142         | 0.8872                  | -                        |
| 9.1667 | 3300 | 15.7132       | 30.1223         | 0.8873                  | -                        |
| 9.4444 | 3400 | 15.2933       | 30.4500         | 0.8870                  | -                        |
| 9.7222 | 3500 | 14.7292       | 30.8898         | 0.8876                  | -                        |
| 10.0   | 3600 | 15.1894       | 30.9508         | 0.8877                  | -                        |
| -1     | -1   | -             | -               | -                       | 0.8678                   |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.4.0.dev0
- Transformers: 4.49.0.dev0
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- Datasets: 3.2.0
- Tokenizers: 0.21.0

## 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",
}
```

#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}
```

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