---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:10K<n<100K
- loss:MultipleNegativesSymmetricRankingLoss
base_model: distilbert/distilbert-base-uncased
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: what is GOGO
  sentences:
  - What is Viasat
  - are we flying into Tel Aviv
  - how do i correct a name in term
- source_sentence: What is EU 261
  sentences:
  - is puj a EU compensation country
  - can i take my bicycle on af
  - flight delays over 6 hours
- source_sentence: Can i get wifi
  sentences:
  - which aircrafts do not have wifi
  - military traveling with pet
  - baggage delay to carousel
- source_sentence: austin airport
  sentences:
  - What time is IAH open
  - amex card free checked bag
  - what is upgrade companion
- source_sentence: pets in cargo
  sentences:
  - can a pet travel in cargo
  - baggage exceptions for Amex
  - how do I get sky priority
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on distilbert/distilbert-base-uncased
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: eval examples
      type: eval_examples
    metrics:
    - type: pearson_cosine
      value: .nan
      name: Pearson Cosine
    - type: spearman_cosine
      value: .nan
      name: Spearman Cosine
    - type: pearson_manhattan
      value: .nan
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: .nan
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: .nan
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: .nan
      name: Spearman Euclidean
    - type: pearson_dot
      value: .nan
      name: Pearson Dot
    - type: spearman_dot
      value: .nan
      name: Spearman Dot
    - type: pearson_max
      value: .nan
      name: Pearson Max
    - type: spearman_max
      value: .nan
      name: Spearman Max
---

# SentenceTransformer based on distilbert/distilbert-base-uncased

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased). 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:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) <!-- at revision 12040accade4e8a0f71eabdb258fecc2e7e948be -->
- **Maximum Sequence Length:** 512 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': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel 
  (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("pjbhaumik/biencoder-finetune-model-v9")
# Run inference
sentences = [
    'pets in cargo',
    'can a pet travel in cargo',
    'baggage exceptions for Amex',
]
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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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

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## Evaluation

### Metrics

#### Semantic Similarity
* Dataset: `eval_examples`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric             | Value   |
|:-------------------|:--------|
| pearson_cosine     | nan     |
| spearman_cosine    | nan     |
| pearson_manhattan  | nan     |
| spearman_manhattan | nan     |
| pearson_euclidean  | nan     |
| spearman_euclidean | nan     |
| pearson_dot        | nan     |
| spearman_dot       | nan     |
| pearson_max        | nan     |
| **spearman_max**   | **nan** |

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## 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.*
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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 15,488 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: 4 tokens</li><li>mean: 10.4 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.14 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>1: 100.00%</li></ul> |
* Samples:
  | sentence_0                                                       | sentence_1                                                                           | label          |
  |:-----------------------------------------------------------------|:-------------------------------------------------------------------------------------|:---------------|
  | <code>how to use a companion certificate on delta.com</code>     | <code>SHOPPING ON DELTA.COM FOR AMEX CERT</code>                                     | <code>1</code> |
  | <code>is jamaica can be booked with companion certificate</code> | <code>what areas can the American Express companion certificate be applied to</code> | <code>1</code> |
  | <code>how do i book award travel on klm</code>                   | <code>can you book an air france ticket with miles</code>                            | <code>1</code> |
* Loss: [<code>MultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 12
- `multi_dataset_batch_sampler`: round_robin

#### 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
- `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`: 12
- `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
<details><summary>Click to expand</summary>

| Epoch   | Step  | Training Loss | eval_examples_spearman_max |
|:-------:|:-----:|:-------------:|:--------------------------:|
| 0.1033  | 100   | -             | nan                        |
| 0.2066  | 200   | -             | nan                        |
| 0.3099  | 300   | -             | nan                        |
| 0.4132  | 400   | -             | nan                        |
| 0.5165  | 500   | 0.7655        | nan                        |
| 0.6198  | 600   | -             | nan                        |
| 0.7231  | 700   | -             | nan                        |
| 0.8264  | 800   | -             | nan                        |
| 0.9298  | 900   | -             | nan                        |
| 1.0     | 968   | -             | nan                        |
| 1.0331  | 1000  | 0.3727        | nan                        |
| 1.1364  | 1100  | -             | nan                        |
| 1.2397  | 1200  | -             | nan                        |
| 1.3430  | 1300  | -             | nan                        |
| 1.4463  | 1400  | -             | nan                        |
| 1.5496  | 1500  | 0.2686        | nan                        |
| 1.6529  | 1600  | -             | nan                        |
| 1.7562  | 1700  | -             | nan                        |
| 1.8595  | 1800  | -             | nan                        |
| 1.9628  | 1900  | -             | nan                        |
| 2.0     | 1936  | -             | nan                        |
| 2.0661  | 2000  | 0.2709        | nan                        |
| 2.1694  | 2100  | -             | nan                        |
| 2.2727  | 2200  | -             | nan                        |
| 2.3760  | 2300  | -             | nan                        |
| 2.4793  | 2400  | -             | nan                        |
| 2.5826  | 2500  | 0.231         | nan                        |
| 2.6860  | 2600  | -             | nan                        |
| 2.7893  | 2700  | -             | nan                        |
| 2.8926  | 2800  | -             | nan                        |
| 2.9959  | 2900  | -             | nan                        |
| 3.0     | 2904  | -             | nan                        |
| 3.0992  | 3000  | 0.2461        | nan                        |
| 3.2025  | 3100  | -             | nan                        |
| 3.3058  | 3200  | -             | nan                        |
| 3.4091  | 3300  | -             | nan                        |
| 3.5124  | 3400  | -             | nan                        |
| 3.6157  | 3500  | 0.2181        | nan                        |
| 3.7190  | 3600  | -             | nan                        |
| 3.8223  | 3700  | -             | nan                        |
| 3.9256  | 3800  | -             | nan                        |
| 4.0     | 3872  | -             | nan                        |
| 4.0289  | 3900  | -             | nan                        |
| 4.1322  | 4000  | 0.2288        | nan                        |
| 4.2355  | 4100  | -             | nan                        |
| 4.3388  | 4200  | -             | nan                        |
| 4.4421  | 4300  | -             | nan                        |
| 4.5455  | 4400  | -             | nan                        |
| 4.6488  | 4500  | 0.2123        | nan                        |
| 4.7521  | 4600  | -             | nan                        |
| 4.8554  | 4700  | -             | nan                        |
| 4.9587  | 4800  | -             | nan                        |
| 5.0     | 4840  | -             | nan                        |
| 5.0620  | 4900  | -             | nan                        |
| 5.1653  | 5000  | 0.2254        | nan                        |
| 5.2686  | 5100  | -             | nan                        |
| 5.3719  | 5200  | -             | nan                        |
| 5.4752  | 5300  | -             | nan                        |
| 5.5785  | 5400  | -             | nan                        |
| 5.6818  | 5500  | 0.2077        | nan                        |
| 5.7851  | 5600  | -             | nan                        |
| 5.8884  | 5700  | -             | nan                        |
| 5.9917  | 5800  | -             | nan                        |
| 6.0     | 5808  | -             | nan                        |
| 6.0950  | 5900  | -             | nan                        |
| 6.1983  | 6000  | 0.218         | nan                        |
| 6.3017  | 6100  | -             | nan                        |
| 6.4050  | 6200  | -             | nan                        |
| 6.5083  | 6300  | -             | nan                        |
| 6.6116  | 6400  | -             | nan                        |
| 6.7149  | 6500  | 0.206         | nan                        |
| 6.8182  | 6600  | -             | nan                        |
| 6.9215  | 6700  | -             | nan                        |
| 7.0     | 6776  | -             | nan                        |
| 7.0248  | 6800  | -             | nan                        |
| 7.1281  | 6900  | -             | nan                        |
| 7.2314  | 7000  | 0.2126        | nan                        |
| 7.3347  | 7100  | -             | nan                        |
| 7.4380  | 7200  | -             | nan                        |
| 7.5413  | 7300  | -             | nan                        |
| 7.6446  | 7400  | -             | nan                        |
| 7.7479  | 7500  | 0.2065        | nan                        |
| 7.8512  | 7600  | -             | nan                        |
| 7.9545  | 7700  | -             | nan                        |
| 8.0     | 7744  | -             | nan                        |
| 8.0579  | 7800  | -             | nan                        |
| 8.1612  | 7900  | -             | nan                        |
| 8.2645  | 8000  | 0.2068        | nan                        |
| 8.3678  | 8100  | -             | nan                        |
| 8.4711  | 8200  | -             | nan                        |
| 8.5744  | 8300  | -             | nan                        |
| 8.6777  | 8400  | -             | nan                        |
| 8.7810  | 8500  | 0.2014        | nan                        |
| 8.8843  | 8600  | -             | nan                        |
| 8.9876  | 8700  | -             | nan                        |
| 9.0     | 8712  | -             | nan                        |
| 9.0909  | 8800  | -             | nan                        |
| 9.1942  | 8900  | -             | nan                        |
| 9.2975  | 9000  | 0.2057        | nan                        |
| 9.4008  | 9100  | -             | nan                        |
| 9.5041  | 9200  | -             | nan                        |
| 9.6074  | 9300  | -             | nan                        |
| 9.7107  | 9400  | -             | nan                        |
| 9.8140  | 9500  | 0.1969        | nan                        |
| 9.9174  | 9600  | -             | nan                        |
| 10.0    | 9680  | -             | nan                        |
| 10.0207 | 9700  | -             | nan                        |
| 10.1240 | 9800  | -             | nan                        |
| 10.2273 | 9900  | -             | nan                        |
| 10.3306 | 10000 | 0.2023        | nan                        |
| 10.4339 | 10100 | -             | nan                        |
| 10.5372 | 10200 | -             | nan                        |
| 10.6405 | 10300 | -             | nan                        |
| 10.7438 | 10400 | -             | nan                        |
| 10.8471 | 10500 | 0.1946        | nan                        |
| 10.9504 | 10600 | -             | nan                        |
| 11.0    | 10648 | -             | nan                        |
| 11.0537 | 10700 | -             | nan                        |
| 11.1570 | 10800 | -             | nan                        |
| 11.2603 | 10900 | -             | nan                        |
| 11.3636 | 11000 | 0.1982        | nan                        |
| 11.4669 | 11100 | -             | nan                        |
| 11.5702 | 11200 | -             | nan                        |
| 11.6736 | 11300 | -             | nan                        |
| 11.7769 | 11400 | -             | nan                        |
| 11.8802 | 11500 | 0.1919        | nan                        |
| 11.9835 | 11600 | -             | nan                        |
| 12.0    | 11616 | -             | nan                        |

</details>

### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.0
- Transformers: 4.41.2
- PyTorch: 2.1.0
- Accelerate: 0.30.1
- Datasets: 2.19.1
- 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",
}
```

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