README.md

---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:10000
- loss:SoftmaxLoss
base_model: google-bert/bert-base-uncased
datasets: []
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: A man selling donuts to a customer during a world exhibition event
    held in the city of Angeles
  sentences:
  - The man is doing tricks.
  - A woman drinks her coffee in a small cafe.
  - The building is made of logs.
- source_sentence: A group of people prepare hot air balloons for takeoff.
  sentences:
  - There are hot air balloons on the ground and air.
  - A man is in an art museum.
  - People watch another person do a trick.
- source_sentence: Three workers are trimming down trees.
  sentences:
  - The goalie is sleeping at home.
  - There are three workers
  - The girl has brown hair.
- source_sentence: Two brown-haired men wearing short-sleeved shirts and shorts are
    climbing stairs.
  sentences:
  - The men have blonde hair.
  - A bicyclist passes an esthetically beautiful building on a sunny day
  - Two men are dancing.
- source_sentence: A man is sitting in on the side of the street with brass pots.
  sentences:
  - a younger boy looks at his father
  - Children are at the beach.
  - a man does not have brass pots
pipeline_tag: sentence-similarity
co2_eq_emissions:
  emissions: 147.28843774992524
  energy_consumed: 0.2758298255748315
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: AMD EPYC 7H12 64-Core Processor
  ram_total_size: 229.14864349365234
  hours_used: 0.351
  hardware_used: 8 x NVIDIA GeForce RTX 3090
model-index:
- name: SentenceTransformer based on google-bert/bert-base-uncased
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev
      type: sts-dev
    metrics:
    - type: pearson_cosine
      value: 0.47725003430658275
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.5475746919034576
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.5043805022296893
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.5420702830995872
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.5083739540394052
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.544209699690841
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.4458579859528435
      name: Pearson Dot
    - type: spearman_dot
      value: 0.4698642508787034
      name: Spearman Dot
    - type: pearson_max
      value: 0.5083739540394052
      name: Pearson Max
    - type: spearman_max
      value: 0.5475746919034576
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test
      type: sts-test
    metrics:
    - type: pearson_cosine
      value: 0.5320947494943107
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.5317279446221387
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.5575308236485216
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.5554390408837996
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.55587770863865
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.5535804159700501
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.2787697886285483
      name: Pearson Dot
    - type: spearman_dot
      value: 0.2710358104528421
      name: Spearman Dot
    - type: pearson_max
      value: 0.5575308236485216
      name: Pearson Max
    - type: spearman_max
      value: 0.5554390408837996
      name: Spearman Max
    - type: pearson_cosine
      value: 0.4493844540252116
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.4694611677633312
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.4773641092320219
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.4763054309792941
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.4796801942910325
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.47774521406648734
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.4081600817978359
      name: Pearson Dot
    - type: spearman_dot
      value: 0.3898881150281674
      name: Spearman Dot
    - type: pearson_max
      value: 0.4796801942910325
      name: Pearson Max
    - type: spearman_max
      value: 0.47774521406648734
      name: Spearman Max
---

# SentenceTransformer based on google-bert/bert-base-uncased

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-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:** [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) <!-- at revision 86b5e0934494bd15c9632b12f734a8a67f723594 -->
- **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: BertModel 
  (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("jilangdi/bert-base-uncased-nli-v1")
# Run inference
sentences = [
    'A man is sitting in on the side of the street with brass pots.',
    'a man does not have brass pots',
    'Children are at the beach.',
]
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

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.4773     |
| **spearman_cosine** | **0.5476** |
| pearson_manhattan   | 0.5044     |
| spearman_manhattan  | 0.5421     |
| pearson_euclidean   | 0.5084     |
| spearman_euclidean  | 0.5442     |
| pearson_dot         | 0.4459     |
| spearman_dot        | 0.4699     |
| pearson_max         | 0.5084     |
| spearman_max        | 0.5476     |

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.5321     |
| **spearman_cosine** | **0.5317** |
| pearson_manhattan   | 0.5575     |
| spearman_manhattan  | 0.5554     |
| pearson_euclidean   | 0.5559     |
| spearman_euclidean  | 0.5536     |
| pearson_dot         | 0.2788     |
| spearman_dot        | 0.271      |
| pearson_max         | 0.5575     |
| spearman_max        | 0.5554     |

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.4494     |
| **spearman_cosine** | **0.4695** |
| pearson_manhattan   | 0.4774     |
| spearman_manhattan  | 0.4763     |
| pearson_euclidean   | 0.4797     |
| spearman_euclidean  | 0.4777     |
| pearson_dot         | 0.4082     |
| spearman_dot        | 0.3899     |
| pearson_max         | 0.4797     |
| spearman_max        | 0.4777     |

<!--
## 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: 10,000 training samples
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | premise                                                                           | hypothesis                                                                       | label                                                              |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           | int                                                                |
  | details | <ul><li>min: 6 tokens</li><li>mean: 17.38 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.7 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>0: ~33.40%</li><li>1: ~33.30%</li><li>2: ~33.30%</li></ul> |
* Samples:
  | premise                                                             | hypothesis                                                     | label          |
  |:--------------------------------------------------------------------|:---------------------------------------------------------------|:---------------|
  | <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is training his horse for a competition.</code> | <code>1</code> |
  | <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is at a diner, ordering an omelette.</code>     | <code>2</code> |
  | <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code>                 | <code>0</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)

### Evaluation Dataset

#### Unnamed Dataset


* Size: 1,000 evaluation samples
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | premise                                                                           | hypothesis                                                                        | label                                                              |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | int                                                                |
  | details | <ul><li>min: 6 tokens</li><li>mean: 18.44 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.57 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>0: ~33.10%</li><li>1: ~33.30%</li><li>2: ~33.60%</li></ul> |
* Samples:
  | premise                                                            | hypothesis                                                                                         | label          |
  |:-------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|:---------------|
  | <code>Two women are embracing while holding to go packages.</code> | <code>The sisters are hugging goodbye while holding to go packages after just eating lunch.</code> | <code>1</code> |
  | <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code>                                                       | <code>0</code> |
  | <code>Two women are embracing while holding to go packages.</code> | <code>The men are fighting outside a deli.</code>                                                  | <code>2</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
- `fp16`: True

#### 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`: 1.0
- `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`: 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
- `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`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | loss   | sts-dev_spearman_cosine | sts-test_spearman_cosine |
|:------:|:----:|:-------------:|:------:|:-----------------------:|:------------------------:|
| 0      | 0    | -             | -      | 0.5931                  | -                        |
| 1.0    | 79   | -             | -      | -                       | 0.5317                   |
| 1.2658 | 100  | 0.545         | 0.9351 | 0.5973                  | -                        |
| 2.5316 | 200  | 0.5286        | 0.9535 | 0.5660                  | -                        |
| 3.7975 | 300  | 0.3553        | 1.0364 | 0.5476                  | -                        |
| 5.0    | 395  | -             | -      | -                       | 0.4695                   |


### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.276 kWh
- **Carbon Emitted**: 0.147 kg of CO2
- **Hours Used**: 0.351 hours

### Training Hardware
- **On Cloud**: No
- **GPU Model**: 8 x NVIDIA GeForce RTX 3090
- **CPU Model**: AMD EPYC 7H12 64-Core Processor
- **RAM Size**: 229.15 GB

### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.1+cu121
- Accelerate: 0.31.0
- Datasets: 2.19.2
- Tokenizers: 0.19.1

## Citation

### BibTeX

#### Sentence Transformers and SoftmaxLoss
```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",
}
```

<!--
## 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.*
-->