README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:160436
- loss:DenoisingAutoEncoderLoss
base_model: google-bert/bert-base-uncased
widget:
- source_sentence: how do i make evolution check and notify new emails , without keeping
    main ui open ?
  sentences:
  - ppas be removed?
  - how set serve as a samba primary controller pam modules to authenticate against?
  - how do make check and notify new emails keeping
- source_sentence: setting http proxy in awesome wm
  sentences:
  - on 10.04 on p series?
  - setting http proxy awesome wm
  - mean package is "set to installed?
- source_sentence: what is ubuntu advantage ?
  sentences:
  - is advantage?
  - how turn calling on f1
  - is utnubu?
- source_sentence: is there a way to check hardware integrity ?
  sentences:
  - is there a way to hardware integrity?
  - to change key ctrl
  - software is to tv card
- source_sentence: how to fix ssl error from python apps ( urllib ) when behind https
    proxy ?
  sentences:
  - windows started with archive
  - upstart
  - how to ssl from python () proxy
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- map
- mrr@10
- ndcg@10
co2_eq_emissions:
  emissions: 74.02946721860093
  energy_consumed: 0.19045301341027557
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
  ram_total_size: 31.777088165283203
  hours_used: 0.64
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: SentenceTransformer based on google-bert/bert-base-uncased
  results:
  - task:
      type: reranking
      name: Reranking
    dataset:
      name: AskUbuntu dev
      type: AskUbuntu-dev
    metrics:
    - type: map
      value: 0.5058158414596666
      name: Map
    - type: mrr@10
      value: 0.6325571254142682
      name: Mrr@10
    - type: ndcg@10
      value: 0.5529143206799554
      name: Ndcg@10
  - task:
      type: reranking
      name: Reranking
    dataset:
      name: AskUbuntu test
      type: AskUbuntu-test
    metrics:
    - type: map
      value: 0.5826205294809574
      name: Map
    - type: mrr@10
      value: 0.7237319322514852
      name: Mrr@10
    - type: ndcg@10
      value: 0.6303658219971641
      name: Ndcg@10
---

# 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:** 75 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': 75, 'do_lower_case': False}) with Transformer model: BertModel 
  (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("tomaarsen/bert-base-uncased-tsdae-askubuntu")
# Run inference
sentences = [
    'how to fix ssl error from python apps ( urllib ) when behind https proxy ?',
    'how to ssl from python () proxy',
    'upstart',
]
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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You can finetune this model on your own dataset.

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## Evaluation

### Metrics

#### Reranking

* Datasets: `AskUbuntu-dev` and `AskUbuntu-test`
* Evaluated with [<code>RerankingEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.RerankingEvaluator)

| Metric  | AskUbuntu-dev | AskUbuntu-test |
|:--------|:--------------|:---------------|
| **map** | **0.5058**    | **0.5826**     |
| mrr@10  | 0.6326        | 0.7237         |
| ndcg@10 | 0.5529        | 0.6304         |

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 160,436 training samples
* Columns: <code>text</code> and <code>noisy</code>
* Approximate statistics based on the first 1000 samples:
  |         | text                                                                              | noisy                                                                            |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           |
  | details | <ul><li>min: 5 tokens</li><li>mean: 14.43 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.47 tokens</li><li>max: 24 tokens</li></ul> |
* Samples:
  | text                                                                                             | noisy                                                        |
  |:-------------------------------------------------------------------------------------------------|:-------------------------------------------------------------|
  | <code>how to get the `` your battery is broken '' message to go away ?</code>                    | <code>to get the is broken go away?</code>                   |
  | <code>how can i set the software center to install software for non-root users ?</code>          | <code>how can i the center install non-root users</code>     |
  | <code>what are some alternatives to upgrading without using the standard upgrade system ?</code> | <code>what are alternatives to using standard system?</code> |
* Loss: [<code>DenoisingAutoEncoderLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#denoisingautoencoderloss)

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

- `eval_strategy`: steps
- `learning_rate`: 3e-05
- `num_train_epochs`: 1
- `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`: 8
- `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`: 3e-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`: 1
- `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`: None
- `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
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step  | Training Loss | AskUbuntu-dev_map | AskUbuntu-test_map |
|:------:|:-----:|:-------------:|:-----------------:|:------------------:|
| -1     | -1    | -             | 0.4151            | -                  |
| 0.0499 | 1000  | 6.1757        | -                 | -                  |
| 0.0997 | 2000  | 4.0925        | -                 | -                  |
| 0.1496 | 3000  | 3.2921        | -                 | -                  |
| 0.1995 | 4000  | 2.9046        | -                 | -                  |
| 0.2493 | 5000  | 2.669         | 0.5158            | -                  |
| 0.2992 | 6000  | 2.5884        | -                 | -                  |
| 0.3490 | 7000  | 2.437         | -                 | -                  |
| 0.3989 | 8000  | 2.3406        | -                 | -                  |
| 0.4488 | 9000  | 2.2709        | -                 | -                  |
| 0.4986 | 10000 | 2.1881        | 0.5131            | -                  |
| 0.5485 | 11000 | 2.1627        | -                 | -                  |
| 0.5984 | 12000 | 2.1055        | -                 | -                  |
| 0.6482 | 13000 | 2.0577        | -                 | -                  |
| 0.6981 | 14000 | 2.0133        | -                 | -                  |
| 0.7479 | 15000 | 1.9877        | 0.5130            | -                  |
| 0.7978 | 16000 | 1.9569        | -                 | -                  |
| 0.8477 | 17000 | 1.9219        | -                 | -                  |
| 0.8975 | 18000 | 1.9124        | -                 | -                  |
| 0.9474 | 19000 | 1.8676        | -                 | -                  |
| 0.9973 | 20000 | 1.8461        | 0.5058            | -                  |
| -1     | -1    | -             | -                 | 0.5826             |


### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.190 kWh
- **Carbon Emitted**: 0.074 kg of CO2
- **Hours Used**: 0.64 hours

### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB

### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 3.4.0.dev0
- Transformers: 4.48.0.dev0
- PyTorch: 2.5.0+cu121
- Accelerate: 0.35.0.dev0
- Datasets: 2.20.0
- Tokenizers: 0.21.0

## 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",
}
```

#### DenoisingAutoEncoderLoss
```bibtex
@inproceedings{wang-2021-TSDAE,
    title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning",
    author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    pages = "671--688",
    url = "https://arxiv.org/abs/2104.06979",
}
```

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