---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:1K<n<10K
- loss:CachedGISTEmbedLoss
base_model: nomic-ai/nomic-embed-text-v1.5
metrics:
- cosine_accuracy
- dot_accuracy
- manhattan_accuracy
- euclidean_accuracy
- max_accuracy
widget:
- source_sentence: Pilot
  sentences:
  - Episode Two
  - dog dinosaur bone
  - 10' x 12' gazebo
- source_sentence: skull
  sentences:
  - cool head s
  - trunk bike rack 4
  - bread without gluten
- source_sentence: pipes
  sentences:
  - chillum pipe
  - Deckle Edge Ruler
  - dog collar for boxer
- source_sentence: ddj400
  sentences:
  - lc27h711qenxza
  - bed frame for full
  - chicago bears gifts
- source_sentence: primes
  sentences:
  - Newton
  - big boys sneakers
  - large dog clothes
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: esci dev
      type: esci-dev
    metrics:
    - type: cosine_accuracy
      value: 0.6414052697616061
      name: Cosine Accuracy
    - type: dot_accuracy
      value: 0.36637390213299875
      name: Dot Accuracy
    - type: manhattan_accuracy
      value: 0.6404015056461732
      name: Manhattan Accuracy
    - type: euclidean_accuracy
      value: 0.6406524466750314
      name: Euclidean Accuracy
    - type: max_accuracy
      value: 0.6414052697616061
      name: Max Accuracy
---

# SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-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:** [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) <!-- at revision 91d2d6bfdddf0b0da840f901b533e99bae30d757 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **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: NomicBertModel 
  (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("sentence_transformers_model_id")
# Run inference
sentences = [
    'primes',
    'Newton',
    'big boys sneakers',
]
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

#### Triplet
* Dataset: `esci-dev`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric             | Value      |
|:-------------------|:-----------|
| cosine_accuracy    | 0.6414     |
| dot_accuracy       | 0.3664     |
| manhattan_accuracy | 0.6404     |
| euclidean_accuracy | 0.6407     |
| **max_accuracy**   | **0.6414** |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 9,090 training samples
* Columns: <code>query</code>, <code>pos</code>, and <code>neg</code>
* Approximate statistics based on the first 1000 samples:
  |         | query                                                                            | pos                                                                               | neg                                                                              |
  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                            | string                                                                           |
  | details | <ul><li>min: 3 tokens</li><li>mean: 7.42 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 29.27 tokens</li><li>max: 87 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 29.8 tokens</li><li>max: 82 tokens</li></ul> |
* Samples:
  | query                                                         | pos                                                                                                                                                                                                                   | neg                                                                                                                                                                                         |
  |:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>1 3/4 inch tooled belt strap without belt buckle</code> | <code>BS3501 Solid Brass Leaf Belt Buckle Fits 1-3/4"(45mm) Wide Belt</code>                                                                                                                                          | <code>Nocona Men's Hired Brown Floral Eagle, 40</code>                                                                                                                                      |
  | <code>7edge phone case peacock</code>                         | <code>Galaxy S7 Edge Case for Girls Women Clear with Flowers Design Shockproof Protective Cell Phone Cases for Samsung Galaxy S7 Edge 5.5 Inch Cute Floral Pattern Print Flexible Slim Fit Bumper Rubber Cover</code> | <code>Galaxy S7 Case, Galaxy S7 Phone Case with HD Screen Protector for Girls Women, Gritup Cute Clear Gradient Glitter Liquid TPU Slim Phone Case for Samsung Galaxy S7 Teal/Purple</code> |
  | <code>girls white shoes</code>                                | <code>adidas Women's Coast Star Shoes, ftwr White/Silver Met./ core Black, 6 M US</code>                                                                                                                              | <code>Converse Optical White M7650 - HI TOP Size 6 M US Women / 4 M US Men</code>                                                                                                           |
* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
  ```json
  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, '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})
    (2): Normalize()
  ), 'temperature': 0.01}
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 3,985 evaluation samples
* Columns: <code>query</code>, <code>pos</code>, and <code>neg</code>
* Approximate statistics based on the first 1000 samples:
  |         | query                                                                            | pos                                                                                | neg                                                                               |
  |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                             | string                                                                            |
  | details | <ul><li>min: 3 tokens</li><li>mean: 7.28 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 28.58 tokens</li><li>max: 116 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 29.26 tokens</li><li>max: 79 tokens</li></ul> |
* Samples:
  | query                                 | pos                                                                                                                                                                                                        | neg                                                                                                                                                                                                           |
  |:--------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>colors for dining room</code>   | <code>AOOS CUSTOM Dimmable LED Neon Signs for Home Bedroom Salon Dining Room Wall Decor (Customization: Texts, Designs, Logos, Languages, Colors, Sizes, Fonts, Color-Changing) (24" / 1 Line Text)</code> | <code>Jetec 5 Pieces EAT Sign Kitchen Wood Rustic Sign Arrow Wall Decor EAT Farmhouse Decoration Hanging Arrow Wooden Sign for Kitchen Wall Home Dining Room (Charming Color)</code>                          |
  | <code>mix no 6 heels for women</code> | <code>DREAM PAIRS Women's Hi-Chunk Gold Glitter High Heel Pump Sandals - 6 M US</code>                                                                                                                     | <code>Fashare Womens High Heels Pointed Toe Bowtie Back Ankle Buckle Strap Wedding Evening Party Dress Pumps Shoes</code>                                                                                     |
  | <code>goxlrmini</code>                | <code>Singing Machine SMM-205 Unidirectional Dynamic Microphone with 10 Ft. Cord,Black, one size</code>                                                                                                    | <code>Behringer U-Phoria Studio Pro Complete Recording Bundle with UMC202HD USB Audio Interface - With 20' 6mm Rubber XLR Microphone Cable, On-Stage MBS5000 Broadcast/Webcast Boom Arm with XLR Cable</code> |
* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
  ```json
  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, '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})
    (2): Normalize()
  ), 'temperature': 0.01}
  ```

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

- `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
- `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
- `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
- `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}
- `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
- `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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | esci-dev_max_accuracy |
|:------:|:----:|:-------------:|:---------------------:|
| 0      | 0    | -             | 0.6414                |
| 0.1757 | 100  | 0.8875        | -                     |
| 0.3515 | 200  | 0.5281        | -                     |
| 0.5272 | 300  | 0.4621        | -                     |
| 0.7030 | 400  | 0.4669        | -                     |
| 0.8787 | 500  | 0.4501        | -                     |
| 1.0545 | 600  | 0.5379        | -                     |
| 1.2302 | 700  | 0.4288        | -                     |
| 1.4060 | 800  | 0.2112        | -                     |
| 1.5817 | 900  | 0.1508        | -                     |
| 1.7575 | 1000 | 0.1133        | -                     |
| 1.9332 | 1100 | 0.1312        | -                     |
| 2.1090 | 1200 | 0.0784        | -                     |
| 2.2847 | 1300 | 0.0983        | -                     |
| 2.4605 | 1400 | 0.106         | -                     |
| 2.6362 | 1500 | 0.1058        | -                     |
| 2.8120 | 1600 | 0.0673        | -                     |
| 2.9877 | 1700 | 0.0355        | -                     |
| 3.1634 | 1800 | 0.0175        | -                     |
| 3.3392 | 1900 | 0.0366        | -                     |
| 3.5149 | 2000 | 0.0332        | -                     |
| 3.6907 | 2100 | 0.0682        | -                     |
| 3.8664 | 2200 | 0.0378        | -                     |
| 4.0422 | 2300 | 0.0239        | -                     |
| 4.2179 | 2400 | 0.0282        | -                     |
| 4.3937 | 2500 | 0.0401        | -                     |
| 4.5694 | 2600 | 0.0268        | -                     |
| 4.7452 | 2700 | 0.0208        | -                     |
| 4.9209 | 2800 | 0.0117        | -                     |
| 5.0967 | 2900 | 0.0045        | -                     |
| 5.2724 | 3000 | 0.0145        | -                     |
| 5.4482 | 3100 | 0.029         | -                     |
| 5.6239 | 3200 | 0.0009        | -                     |
| 5.7996 | 3300 | 0.0033        | -                     |
| 5.9754 | 3400 | 0.0088        | -                     |
| 6.1511 | 3500 | 0.0014        | -                     |
| 6.3269 | 3600 | 0.0027        | -                     |
| 6.5026 | 3700 | 0.0021        | -                     |
| 6.6784 | 3800 | 0.0001        | -                     |
| 6.8541 | 3900 | 0.0025        | -                     |
| 7.0299 | 4000 | 0.0059        | -                     |
| 7.2056 | 4100 | 0.0025        | -                     |
| 7.3814 | 4200 | 0.0029        | -                     |
| 7.5571 | 4300 | 0.0007        | -                     |
| 7.7329 | 4400 | 0.0018        | -                     |
| 7.9086 | 4500 | 0.0032        | -                     |
| 8.0844 | 4600 | 0.0007        | -                     |
| 8.2601 | 4700 | 0.0027        | -                     |
| 8.4359 | 4800 | 0.0027        | -                     |
| 8.6116 | 4900 | 0.0           | -                     |
| 8.7873 | 5000 | 0.0025        | -                     |
| 8.9631 | 5100 | 0.0025        | -                     |
| 9.1388 | 5200 | 0.0014        | -                     |
| 9.3146 | 5300 | 0.0027        | -                     |
| 9.4903 | 5400 | 0.0021        | -                     |
| 9.6661 | 5500 | 0.0           | -                     |
| 9.8418 | 5600 | 0.0025        | -                     |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.0
- Transformers: 4.38.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.27.2
- Datasets: 2.19.1
- Tokenizers: 0.15.2

## 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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