sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:11002 loss:MultipleNegativesRankingLoss custom_code en dataset:bwang0911/word-orders-triplet dataset:jinaai/negation-dataset arxiv:1908.10084 arxiv:1705.00652 base_model:jinaai/jina-embeddings-v2-base-en base_model:finetune:jinaai/jina-embeddings-v2-base-en autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: jinaai/jina-embeddings-v2-base-en
datasets:
- bwang0911/word-orders-triplet
- jinaai/negation-dataset
language:
- en
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:11002
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Man jumps alone on a desert road with mountains in the background.
  sentences:
  - A man jumps on the desert road
  - A man plays a silver electric guitar.
  - A man doesnt jump on the desert road
- source_sentence: Players from two teams tangle together in pursuit of a flying rugby
    ball.
  sentences:
  - Two teams playing.
  - Two teams not playing.
  - Men are dancing in the street.
- source_sentence: The team won the game in the final minute.
  sentences:
  - In the final minute, the team won the game.
  - The team lost the game in the final minute.
  - For their anniversary, they took a hike through the mountains, enjoying the peace
    and quiet of nature.
- source_sentence: He finished reading the book in one sitting.
  sentences:
  - He struggled to finish the book and took a week to read it.
  - In one sitting, he finished reading the book.
  - jazz players create spontaneous superior orchestra
- source_sentence: Paint preserves wood
  sentences:
  - Coating protects timber
  - timber coating protects
  - Single cell life came before complex creatures
---

# SentenceTransformer based on jinaai/jina-embeddings-v2-base-en

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) on the [word_orders](https://huggingface.co/datasets/bwang0911/word-orders-triplet) and [negation_dataset](https://huggingface.co/datasets/jinaai/negation-dataset) datasets. 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:** [jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) <!-- at revision 6e85f575bc273f1fd840a658067d0157933c83f0 -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
    - [word_orders](https://huggingface.co/datasets/bwang0911/word-orders-triplet)
    - [negation_dataset](https://huggingface.co/datasets/jinaai/negation-dataset)
- **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': 128, 'do_lower_case': False}) with Transformer model: JinaBertModel 
  (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})
  (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("bwang0911/word-order-jina")
# Run inference
sentences = [
    'Paint preserves wood',
    'Coating protects timber',
    'timber coating protects',
]
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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## Training Details

### Training Datasets

#### word_orders

* Dataset: [word_orders](https://huggingface.co/datasets/bwang0911/word-orders-triplet) at [99609ac](https://huggingface.co/datasets/bwang0911/word-orders-triplet/tree/99609ac84ce5ad127591d7e722564a064cf80a76)
* Size: 1,002 training samples
* Columns: <code>anchor</code>, <code>pos</code>, and <code>neg</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | pos                                                                              | neg                                                                               |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           | string                                                                            |
  | details | <ul><li>min: 5 tokens</li><li>mean: 12.34 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.1 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.51 tokens</li><li>max: 24 tokens</li></ul> |
* Samples:
  | anchor                                                     | pos                                                       | neg                                                        |
  |:-----------------------------------------------------------|:----------------------------------------------------------|:-----------------------------------------------------------|
  | <code>The river flows from the mountains to the sea</code> | <code>Water travels from mountain peaks to ocean</code>   | <code>The river flows from the sea to the mountains</code> |
  | <code>Train departs London for Paris</code>                | <code>Railway journey from London heading to Paris</code> | <code>Train departs Paris for London</code>                |
  | <code>Cargo ship sails from Shanghai to Singapore</code>   | <code>Maritime route Shanghai to Singapore</code>         | <code>Cargo ship sails from Singapore to Shanghai</code>   |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20,
      "similarity_fct": "cos_sim"
  }
  ```

#### negation_dataset

* Dataset: [negation_dataset](https://huggingface.co/datasets/jinaai/negation-dataset) at [cd02256](https://huggingface.co/datasets/jinaai/negation-dataset/tree/cd02256426cc566d176285a987e5436f1cd01382)
* Size: 10,000 training samples
* Columns: <code>anchor</code>, <code>entailment</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | entailment                                                                       | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           | string                                                                            |
  | details | <ul><li>min: 6 tokens</li><li>mean: 16.48 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.63 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.46 tokens</li><li>max: 32 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                   | entailment                                           | negative                                                 |
  |:-------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------|:---------------------------------------------------------|
  | <code>Two young girls are playing outside in a non-urban environment.</code>                                             | <code>Two girls are playing outside.</code>          | <code>Two girls are not playing outside.</code>          |
  | <code>A man with a red shirt is watching another man who is standing on top of a attached cart filled to the top.</code> | <code>A man is standing on top of a cart.</code>     | <code>A man is not standing on top of a cart.</code>     |
  | <code>A man in a blue shirt driving a Segway type vehicle.</code>                                                        | <code>A person is riding a motorized vehicle.</code> | <code>A person is not riding a motorized vehicle.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20,
      "similarity_fct": "cos_sim"
  }
  ```

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

- `per_device_train_batch_size`: 128
- `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`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 8
- `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`: 3
- `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`: 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
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.1149 | 10   | 2.0411        |
| 0.2299 | 20   | 1.5167        |
| 0.3448 | 30   | 0.64          |
| 0.4598 | 40   | 0.6058        |
| 0.5747 | 50   | 0.6042        |
| 0.6897 | 60   | 0.4193        |
| 0.8046 | 70   | 0.5208        |
| 0.9195 | 80   | 0.4864        |
| 1.0345 | 90   | 0.4145        |
| 1.1494 | 100  | 0.69          |
| 1.2644 | 110  | 0.9602        |
| 1.3793 | 120  | 0.2539        |
| 1.4943 | 130  | 0.2558        |
| 1.6092 | 140  | 0.2769        |
| 1.7241 | 150  | 0.2154        |
| 1.8391 | 160  | 0.293         |
| 1.9540 | 170  | 0.2598        |
| 2.0690 | 180  | 0.2113        |
| 2.1839 | 190  | 0.9366        |
| 2.2989 | 200  | 0.2121        |
| 2.4138 | 210  | 0.1486        |
| 2.5287 | 220  | 0.1765        |
| 2.6437 | 230  | 0.1438        |
| 2.7586 | 240  | 0.1589        |
| 2.8736 | 250  | 0.1869        |
| 2.9885 | 260  | 0.1682        |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.46.0
- PyTorch: 2.5.1+cu124
- Accelerate: 1.1.1
- Datasets: 3.1.0
- Tokenizers: 0.20.1

## 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",
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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