sentence-transformers safetensors new sentence-similarity feature-extraction generated_from_trainer dataset_size:84524 loss:AttributeTripletLoss custom_code arxiv:1908.10084 arxiv:1703.07737 base_model:Alibaba-NLP/gte-base-en-v1.5 base_model:finetune:Alibaba-NLP/gte-base-en-v1.5 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: Alibaba-NLP/gte-base-en-v1.5
library_name: sentence-transformers
metrics:
- cosine_accuracy
- silhouette_cosine
- silhouette_euclidean
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:84524
- loss:AttributeTripletLoss
widget:
- source_sentence: Don Piper
  sentences:
  - Tommy Nelson
  - Kate Walbert
  - publisher
  - author
- source_sentence: The Luxe
  sentences:
  - '1999'
  - publication_date
  - title
  - 'Critical Care, Mercy Hospital Series #1'
- source_sentence: Bram Stoker
  sentences:
  - author
  - Michael J. Pangio
  - '9781598871012'
  - isbn_13
- source_sentence: '9780385340557'
  sentences:
  - BBC Books
  - '9780399208539'
  - author
  - isbn_13
- source_sentence: Midnight
  sentences:
  - The Bone Parade
  - 12/01/2005
  - publication_date
  - title
model-index:
- name: SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: cosine_accuracy
      value: 0.8125
      name: Cosine Accuracy
    - type: cosine_accuracy
      value: 0.8182253837585449
      name: Cosine Accuracy
  - task:
      type: silhouette
      name: Silhouette
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: silhouette_cosine
      value: -0.1701439768075943
      name: Silhouette Cosine
    - type: silhouette_euclidean
      value: -0.15889228880405426
      name: Silhouette Euclidean
    - type: silhouette_cosine
      value: -0.15601858496665955
      name: Silhouette Cosine
    - type: silhouette_euclidean
      value: -0.1558564007282257
      name: Silhouette Euclidean
---

# SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-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:** [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) <!-- at revision a829fd0e060bb84554da0dfd354d0de0f7712b7f -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **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: NewModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("albertus-sussex/veriscrape-book-test-sbert-bs128_lr0.0001_ep5_cosine_snTrue_spFalse_hn1")
# Run inference
sentences = [
    'Midnight',
    'The Bone Parade',
    '12/01/2005',
]
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]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

## Evaluation

### Metrics

#### Triplet

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

| Metric              | Value      |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.8125** |

#### Silhouette

* Evaluated with <code>veriscrape.training.SilhouetteEvaluator</code>

| Metric                | Value       |
|:----------------------|:------------|
| **silhouette_cosine** | **-0.1701** |
| silhouette_euclidean  | -0.1589     |

#### Triplet

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

| Metric              | Value      |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.8182** |

#### Silhouette

* Evaluated with <code>veriscrape.training.SilhouetteEvaluator</code>

| Metric                | Value      |
|:----------------------|:-----------|
| **silhouette_cosine** | **-0.156** |
| silhouette_euclidean  | -0.1559    |

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 84,524 training samples
* Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, <code>pos_attr_name</code>, and <code>neg_attr_name</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                           | positive                                                                         | negative                                                                         | pos_attr_name                                                                   | neg_attr_name                                                                  |
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                           | string                                                                           | string                                                                          | string                                                                         |
  | details | <ul><li>min: 3 tokens</li><li>mean: 6.97 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 7.09 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.31 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.77 tokens</li><li>max: 5 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.8 tokens</li><li>max: 5 tokens</li></ul> |
* Samples:
  | anchor                     | positive                   | negative                                  | pos_attr_name                 | neg_attr_name          |
  |:---------------------------|:---------------------------|:------------------------------------------|:------------------------------|:-----------------------|
  | <code>09/01/1997</code>    | <code>12/01/1977</code>    | <code>2010</code>                         | <code>publication_date</code> | <code>title</code>     |
  | <code>9780060275730</code> | <code>9780829748772</code> | <code>HarperCollins Publishers Ltd</code> | <code>isbn_13</code>          | <code>publisher</code> |
  | <code>9780609809648</code> | <code>9780764551956</code> | <code>HarperCollins Publishers</code>     | <code>isbn_13</code>          | <code>author</code>    |
* Loss: <code>veriscrape.training.AttributeTripletLoss</code> with these parameters:
  ```json
  {
      "distance_metric": "TripletDistanceMetric.COSINE",
      "triplet_margin": 5
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset

* Size: 9,392 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, <code>pos_attr_name</code>, and <code>neg_attr_name</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                           | positive                                                                         | negative                                                                         | pos_attr_name                                                                   | neg_attr_name                                                                  |
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                           | string                                                                           | string                                                                          | string                                                                         |
  | details | <ul><li>min: 3 tokens</li><li>mean: 6.85 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.98 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.08 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.75 tokens</li><li>max: 5 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.8 tokens</li><li>max: 5 tokens</li></ul> |
* Samples:
  | anchor                         | positive                     | negative                   | pos_attr_name          | neg_attr_name                 |
  |:-------------------------------|:-----------------------------|:---------------------------|:-----------------------|:------------------------------|
  | <code>9780764200564</code>     | <code>: 9780590458467</code> | <code>1984</code>          | <code>isbn_13</code>   | <code>publication_date</code> |
  | <code>Penguin Group USA</code> | <code>Signet</code>          | <code>9781600243912</code> | <code>publisher</code> | <code>isbn_13</code>          |
  | <code>Alphabet Juice</code>    | <code>Space</code>           | <code>9780807871133</code> | <code>title</code>     | <code>isbn_13</code>          |
* Loss: <code>veriscrape.training.AttributeTripletLoss</code> with these parameters:
  ```json
  {
      "distance_metric": "TripletDistanceMetric.COSINE",
      "triplet_margin": 5
  }
  ```

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

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `learning_rate`: 0.0001
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `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`: 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`: 5
- `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`: False
- `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
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch | Step | Training Loss | Validation Loss | cosine_accuracy | silhouette_cosine |
|:-----:|:----:|:-------------:|:---------------:|:---------------:|:-----------------:|
| -1    | -1   | -             | -               | 0.4284          | 0.1492            |
| 1.0   | 661  | 3.911         | 3.7903          | 0.8325          | -0.1771           |
| 2.0   | 1322 | 3.8064        | 3.8408          | 0.8343          | -0.1694           |
| 3.0   | 1983 | 3.8565        | 3.8321          | 0.8169          | -0.1698           |
| 4.0   | 2644 | 3.8219        | 3.8325          | 0.8182          | -0.1701           |
| 5.0   | 3305 | 3.8205        | 3.8324          | 0.8125          | -0.1701           |
| -1    | -1   | -             | -               | 0.8182          | -0.1560           |


### Framework Versions
- Python: 3.10.16
- Sentence Transformers: 3.4.1
- Transformers: 4.45.2
- PyTorch: 2.5.1+cu124
- Accelerate: 1.5.2
- Datasets: 3.1.0
- Tokenizers: 0.20.3

## 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",
}
```

#### AttributeTripletLoss
```bibtex
@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->