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---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:300000
- loss:DenoisingAutoEncoderLoss
base_model: FacebookAI/roberta-base
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: free in spain? Are Spain free Motorways toll-free Spain, renewing
old concessions coming
sentences:
- how to calculate weighted grade percentage in excel? To find the grade, multiply
the grade for each assignment against the weight, and then add these totals all
up. So for each cell (in the Total column) we will enter =SUM(Grade Cell * Weight
Cell), so my first formula is =SUM(B2*C2), the next one would be =SUM(B3*C3) and
so on.
- In Red Dead Redemption 2's story mode, players have to go to "Story" in the menu
and then click the save icon from there. However, in Red Dead Online, there is
no such option. On the contrary, players have no way to manually save their game,
which is pretty much par for the course in an online multiplayer experience.
- are motorways free in spain? Are motorways in Spain free? Motorways are 90% toll-free
in Spain. Since 2018, Spain isn't renewing old concessions coming to end.
- source_sentence: things do fort wayne?
sentences:
- what is the difference between a z71 and a 4x4? A Z71 has more undercarriage protection
(more skid plates) and heavier duty shock absorbers/struts for off road use than
a 4X4. Other than that the two are pretty much the same.
- is suboxone bad for kidneys?
- indoor things to do in fort wayne indiana?
- source_sentence: a should hair?
sentences:
- how many times in a week should you shampoo your hair?
- Sujith fell into the borewell on Friday around 5:45 pm while playing on the family's
farm. Initially, he was trapped at a depth of 26 feet but slipped to 88 feet during
attempts to pull him up by tying ropes around his hands. Sujith Wilson fell into
a borewell in Tamil Nadu's Trichy on Friday.
- how to calculate out retained earnings on balance sheet? The retained earnings
are calculated by adding net income to (or subtracting net losses from) the previous
term's retained earnings and then subtracting any net dividend(s) paid to the
shareholders. The figure is calculated at the end of each accounting period (quarterly/annually.)
- source_sentence: long period does go
sentences:
- if someone blocked your email will you know? You could, indeed, be blocked It's
certainly possible that your recipient has blocked you. All that means is that
email from your email address is automatically discarded at that recipient's end.
You will not get a notification; there's simply no way to tell that this has happened.
- is drinking apple cider vinegar every day bad for you?
- how long after period does weight go down?
- source_sentence: beer wine both sugar alcohol excessive be a infections You also
sweets, along with foods moldy cheese, if you prone.
sentences:
- how long does it take to get xfinity internet? Installation generally takes between
two to four hours.
- They began selling the plush animals to retailers rather than operating a store
themselves. Today, Boyds is a publicly traded company that manufactures 18 million-20
million bears a year, all at a government-owned facility in China.
- Since beer and wine both contain yeast and sugar (alcohol is sugar fermented by
yeast), excessive drinking can definitely be a recipe for yeast infections. You
should also go easy on sweets, along with foods like moldy cheese, mushrooms,
and anything fermented if you're prone to yeast infections. 3.
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on FacebookAI/roberta-base
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.6885553993934473
name: Pearson Cosine
- type: spearman_cosine
value: 0.6912117328249255
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6728262252927975
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6724759418767672
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6693578420498989
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6690698040856067
name: Spearman Euclidean
- type: pearson_dot
value: 0.18975985891617667
name: Pearson Dot
- type: spearman_dot
value: 0.1786146878048478
name: Spearman Dot
- type: pearson_max
value: 0.6885553993934473
name: Pearson Max
- type: spearman_max
value: 0.6912117328249255
name: Spearman Max
---
# SentenceTransformer based on FacebookAI/roberta-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base). 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:** [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) <!-- at revision e2da8e2f811d1448a5b465c236feacd80ffbac7b -->
- **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: RobertaModel
(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("bobox/RoBERTa-base-unsupervised-TSDAE")
# Run inference
sentences = [
'beer wine both sugar alcohol excessive be a infections You also sweets, along with foods moldy cheese, if you prone.',
"Since beer and wine both contain yeast and sugar (alcohol is sugar fermented by yeast), excessive drinking can definitely be a recipe for yeast infections. You should also go easy on sweets, along with foods like moldy cheese, mushrooms, and anything fermented if you're prone to yeast infections. 3.",
'They began selling the plush animals to retailers rather than operating a store themselves. Today, Boyds is a publicly traded company that manufactures 18 million-20 million bears a year, all at a government-owned facility in China.',
]
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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</details>
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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: `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 | 0.6886 |
| **spearman_cosine** | **0.6912** |
| pearson_manhattan | 0.6728 |
| spearman_manhattan | 0.6725 |
| pearson_euclidean | 0.6694 |
| spearman_euclidean | 0.6691 |
| pearson_dot | 0.1898 |
| spearman_dot | 0.1786 |
| pearson_max | 0.6886 |
| spearman_max | 0.6912 |
<!--
## 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: 300,000 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 19.88 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 46.45 tokens</li><li>max: 157 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>us have across domestic shorthair, a cat pedigreed one between two breeds Unlike domestic shorthairs which come in of looks, Shorthair kittens the distinct</code> | <code>Most of us have either lived with or come across a domestic shorthair, a cat that closely resembles the pedigreed American Shorthair. The one difference between the two breeds: Unlike domestic shorthairs, which come in a variety of looks, the American Shorthair produces kittens with the same distinct appearance.</code> |
| <code>much cost to get plugs normal with plugs, cost start $120 or if precious plugs are $150 to 200+ . 6 8 will price more required</code> | <code>how much does it cost to get your spark plugs changed? On a normal 4-cylinder engine with standard spark plugs, replacement cost can start around $120 up to $150+, or if precious metal spark plugs are required, $150 up to $200+. 6 cylinder and 8 Cylinder engines will increase in price, as more spark plugs are required.</code> |
| <code>much my paycheck state income%, your income level not tax rate you is of just that a flat tax rate, those, it has the</code> | <code>how much taxes are taken out of my paycheck pa? Pennsylvania levies a flat state income tax rate of 3.07%. Therefore, your income level and filing status will not affect the income tax rate you pay at the state level. Pennsylvania is one of just eight states that has a flat income tax rate, and of those states, it has the lowest rate.</code> |
* Loss: [<code>DenoisingAutoEncoderLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#denoisingautoencoderloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 12
- `per_device_eval_batch_size`: 12
- `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`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 12
- `per_device_eval_batch_size`: 12
- `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 | sts-test_spearman_cosine |
|:-----:|:-----:|:-------------:|:------------------------:|
| 0.02 | 500 | 7.1409 | - |
| 0.04 | 1000 | 6.207 | - |
| 0.05 | 1250 | - | 0.6399 |
| 0.06 | 1500 | 5.8038 | - |
| 0.08 | 2000 | 5.4963 | - |
| 0.1 | 2500 | 5.2609 | 0.6799 |
| 0.12 | 3000 | 5.0997 | - |
| 0.14 | 3500 | 5.0004 | - |
| 0.15 | 3750 | - | 0.7012 |
| 0.16 | 4000 | 4.8694 | - |
| 0.18 | 4500 | 4.7805 | - |
| 0.2 | 5000 | 4.6776 | 0.7074 |
| 0.22 | 5500 | 4.5757 | - |
| 0.24 | 6000 | 4.4598 | - |
| 0.25 | 6250 | - | 0.7185 |
| 0.26 | 6500 | 4.3865 | - |
| 0.28 | 7000 | 4.2692 | - |
| 0.3 | 7500 | 4.2224 | 0.7205 |
| 0.32 | 8000 | 4.1347 | - |
| 0.34 | 8500 | 4.0536 | - |
| 0.35 | 8750 | - | 0.7239 |
| 0.36 | 9000 | 4.0242 | - |
| 0.38 | 9500 | 4.0193 | - |
| 0.4 | 10000 | 3.9166 | 0.7153 |
| 0.42 | 10500 | 3.9004 | - |
| 0.44 | 11000 | 3.8372 | - |
| 0.45 | 11250 | - | 0.7141 |
| 0.46 | 11500 | 3.8037 | - |
| 0.48 | 12000 | 3.7788 | - |
| 0.5 | 12500 | 3.7191 | 0.7078 |
| 0.52 | 13000 | 3.7036 | - |
| 0.54 | 13500 | 3.6697 | - |
| 0.55 | 13750 | - | 0.7095 |
| 0.56 | 14000 | 3.6629 | - |
| 0.58 | 14500 | 3.639 | - |
| 0.6 | 15000 | 3.6048 | 0.7060 |
| 0.62 | 15500 | 3.6072 | - |
| 0.64 | 16000 | 3.574 | - |
| 0.65 | 16250 | - | 0.7056 |
| 0.66 | 16500 | 3.5423 | - |
| 0.68 | 17000 | 3.5379 | - |
| 0.7 | 17500 | 3.5222 | 0.6969 |
| 0.72 | 18000 | 3.5076 | - |
| 0.74 | 18500 | 3.5025 | - |
| 0.75 | 18750 | - | 0.6959 |
| 0.76 | 19000 | 3.4943 | - |
| 0.78 | 19500 | 3.475 | - |
| 0.8 | 20000 | 3.4874 | 0.6946 |
| 0.82 | 20500 | 3.4539 | - |
| 0.84 | 21000 | 3.4704 | - |
| 0.85 | 21250 | - | 0.6942 |
| 0.86 | 21500 | 3.4689 | - |
| 0.88 | 22000 | 3.4617 | - |
| 0.9 | 22500 | 3.4471 | 0.6917 |
| 0.92 | 23000 | 3.4541 | - |
| 0.94 | 23500 | 3.4394 | - |
| 0.95 | 23750 | - | 0.6915 |
| 0.96 | 24000 | 3.4505 | - |
| 0.98 | 24500 | 3.4533 | - |
| 1.0 | 25000 | 3.4574 | 0.6912 |
### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2
- Accelerate: 0.31.0
- Datasets: 2.19.2
- 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",
}
```
#### 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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