---
base_model: UKPLab/triple-encoders-dailydialog
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:985575
- loss:CosineSimilarityTripleEncoderLoss
- loss:ContrastiveLoss
widget:
- source_sentence: A small white and tan dog licking up peanut butter.
  sentences:
  - Someone is making dinner in the kitchen.
  - Someone put peanut butter on the dog's nose because that's always good for a laugh.
  - Two dogs are eating food from a bowl in a kitchen
- source_sentence: A person in a heavy coat shoveling snow.
  sentences:
  - Someone is holding a rocket launcher.
  - An old person is shoveling snow.
  - The private bar's pro bono work was supported by the judges.
- source_sentence: '[B1] [O] [BEFORE]  '
  sentences:
  - '[B2] [E] [BEFORE]  '
  - '[B2] [O] [BEFORE] e'
  - '[AFTER] u'
- source_sentence: '[B1] [E] [BEFORE] e'
  sentences:
  - '[B2] [O] [BEFORE] :'
  - '[B2] [O] [BEFORE] t'
  - '[AFTER] C'
- source_sentence: '[B1] [O] [BEFORE] s'
  sentences:
  - '[B2] [O] [BEFORE] o'
  - '[B2] [E] [BEFORE]  '
  - '[AFTER] u'
---

# SentenceTransformer based on UKPLab/triple-encoders-dailydialog

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [UKPLab/triple-encoders-dailydialog](https://huggingface.co/UKPLab/triple-encoders-dailydialog). 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:** [UKPLab/triple-encoders-dailydialog](https://huggingface.co/UKPLab/triple-encoders-dailydialog) <!-- at revision 390bfe14e21b0eb89068887d1032afcb4f2a1b27 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 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': 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("abhiraj1/eval_triple_encoder")
# Run inference
sentences = [
    '[B1] [O] [BEFORE] s',
    '[B2] [E] [BEFORE]  ',
    '[AFTER] u',
]
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]
```

<!--
### 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.*
-->

<!--
## 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 Datasets

#### Unnamed Dataset


* Size: 43,506 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, <code>sentence_2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                      | sentence_1                                                                      | sentence_2                                                                      | label                                                         |
  |:--------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------|
  | type    | string                                                                          | string                                                                          | string                                                                          | float                                                         |
  | details | <ul><li>min: 5 tokens</li><li>mean: 5.86 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 5.84 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.81 tokens</li><li>max: 4 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.2</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence_0                       | sentence_1                       | sentence_2             | label                           |
  |:---------------------------------|:---------------------------------|:-----------------------|:--------------------------------|
  | <code>[B1] [O] [BEFORE]  </code> | <code>[B2] [E] [BEFORE]  </code> | <code>[AFTER] u</code> | <code>0.0</code>                |
  | <code>[B1] [E] [BEFORE] e</code> | <code>[B2] [O] [BEFORE] :</code> | <code>[AFTER] C</code> | <code>0.0</code>                |
  | <code>[B1] [O] [BEFORE] s</code> | <code>[B2] [E] [BEFORE]  </code> | <code>[AFTER] u</code> | <code>0.6000000000000001</code> |
* Loss: <code>triple_encoders.losses.CosineSimilarityTripleEncoderLoss.CosineSimilarityTripleEncoderLoss</code>

#### Unnamed Dataset


* Size: 942,069 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                         | sentence_1                                                                        | label                                                              |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            | int                                                                |
  | details | <ul><li>min: 5 tokens</li><li>mean: 20.26 tokens</li><li>max: 182 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.94 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>0: ~32.40%</li><li>1: ~33.70%</li><li>2: ~33.90%</li></ul> |
* Samples:
  | sentence_0                                                                                               | sentence_1                                                                         | label          |
  |:---------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------|
  | <code>And the reason Lincoln and his goons had shown up? Well, not everybody was full of respect.</code> | <code>Lincoln didn't show up.</code>                                               | <code>0</code> |
  | <code>a rally car driving down a roadway with people on the side taking pictures</code>                  | <code>People on the side of road taking picture of a rally car driving down</code> | <code>1</code> |
  | <code>The dog is wearing a purple cape.</code>                                                           | <code>THE ANIMAL IS IN A PAGEANT</code>                                            | <code>2</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
  ```json
  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }
  ```

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

- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `multi_dataset_batch_sampler`: round_robin

#### 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`: 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
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch  | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.0919 | 500  | 0.0838        |
| 0.1838 | 1000 | 0.0474        |
| 0.2757 | 1500 | 0.0414        |
| 0.3676 | 2000 | 0.0417        |
| 0.4596 | 2500 | 0.042         |
| 0.5515 | 3000 | 0.0423        |
| 0.6434 | 3500 | 0.0408        |
| 0.7353 | 4000 | 0.0427        |
| 0.8272 | 4500 | 0.0414        |
| 0.9191 | 5000 | 0.0415        |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.0+cu121
- Accelerate: 0.32.1
- Datasets: 2.20.0
- 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",
}
```

#### ContrastiveLoss
```bibtex
@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)}, 
    title={Dimensionality Reduction by Learning an Invariant Mapping}, 
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}
```

<!--
## 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.*
-->