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