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---
base_model: sentence-transformers/all-mpnet-base-v2
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:178829
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: who was actor larry parks
sentences:
- American stage and movie actor.e eventually did so in tears, only to be blacklisted
anyway.
- 'A possum (plural form: possums) is any of about 70 small-to medium-sized arboreal
marsupial species native to Australia, New Guinea, and Sulawesi (and introduced
to New Zealand and China). The common brushtail possum was introduced to New Zealand
by European settlers in an attempt to establish a fur industry. There are no native
predators of the possum in New Zealand, so its numbers in New Zealand have risen
to the point where it is considered a serious pest.'
- A document used to change one or more minor provisions of a living trust or joint
living trust as an alternative to preparing a new living trust.
- source_sentence: what is the salary of a person with a biology degree
sentences:
- $10 to $25 per hour.
- $25,290 (2014-2015 academic year)
- Biology majors who don’t attend a graduate program make a median salary of $51,000
per year, which is a little below the median salary for graduates from all other
majors combined. Don’t let that fact stop you from pursuing a degree in biology
if it’s what you’re passionate about, though. Career Options for Biology Majors.
Below is a list of common career options for biology majors. This isn’t a comprehensive
list, as students who major in biology go on to do many interesting things. However,
this list should give you an idea of the types of work that would be available
to you with a degree in biology.
- source_sentence: definition of pretext
sentences:
- Peanut butter is an excellent source of nutrition. Required to contain at least
90 percent peanuts, it includes more than 30 vitamins and minerals. Peanut butter
contains no cholesterol or trans fats, according to the National Peanut Board.
In fact, studies show that peanut butter may even improve your levels of good
cholesterol.
- Pretext generally refers to a reason for an action which is false, and offered
to cover up true motives or intentions. It is a concept sometimes brought up in
the context of employment discrimination.
- 20.5 degrees Celsius (68.8 degrees Fahrenheit).
- source_sentence: what is cyber spoofing
sentences:
- Once your question has been posted for at least 1 hour and has at least one answer,
click on 'Award Best Answer' button next to your chosen answer. 1 Upload failed.
2 Please upload a file larger than 100x100 pixels. 3 We are experiencing some
problems, please try again.
- Though some vegetable sources of protein contain sufficient values of all essential
amino acids, many are lower in one or more essential amino acids than animal sources,
especially lysine, and to a lesser extent methionine and threonine. 1 Proteins
derived from plant foods (legumes, seeds, grains, and vegetables) can be complete
as well (examples include chickpeas, black beans, pumpkin seeds, cashews, cauliflower,
quinoa, pistachios, turnip greens, black-eyed peas, and soy). 2 Most plant foods
tend to have less of one or more essential amino acid
- A spoofing attack is a situation in which one person or program successfully masquerades
as another by falsifying data and thereby gaining an illegitimate advantage.
- source_sentence: what type of reaction is iron plus oxygen
sentences:
- Pearl
- 'Yes'
- 'When a metal undergos a combination reaction with oxygen, a metal oxide is formed
(similarily, a metal halide is formed if reacted with one of the halogens). You
see the products of this type of reaction whenever you see rust. Rust is the product
of a combination reaction of iron and oxygen: '
---
# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 84f2bcc00d77236f9e89c8a360a00fb1139bf47d -->
- **Maximum Sequence Length:** 384 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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(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})
(2): Normalize()
)
```
## 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("DashReza7/all-mpnet-base-v2_FINETUNED")
# Run inference
sentences = [
'what type of reaction is iron plus oxygen',
'When a metal undergos a combination reaction with oxygen, a metal oxide is formed (similarily, a metal halide is formed if reacted with one of the halogens). You see the products of this type of reaction whenever you see rust. Rust is the product of a combination reaction of iron and oxygen: ',
'Pearl',
]
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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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 178,829 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 9.37 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 60.48 tokens</li><li>max: 197 tokens</li></ul> |
* Samples:
| anchor | positive |
|:------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>what is rba</code> | <code>Results-Based Accountability is a disciplined way of thinking and taking action that communities can use to improve the lives of children, youth, families, adults and the community as a whole.</code> |
| <code>what is rba</code> | <code>Results-Based Accountability® (also known as RBA) is a disciplined way of thinking and taking action that communities can use to improve the lives of children, youth, families, adults and the community as a whole. RBA is also used by organizations to improve the performance of their programs. Creating Community Impact with RBA. Community impact focuses on conditions of well-being for children, families and the community as a whole that a group of leaders is working collectively to improve. For example: “Residents with good jobs,” “Children ready for school,” or “A safe and clean neighborhood”.</code> |
| <code>was ronald reagan a democrat</code> | <code>Yes</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 2e-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`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-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`: 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
- `eval_on_start`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.1789 | 500 | 0.279 |
| 0.3578 | 1000 | 0.2194 |
| 0.5367 | 1500 | 0.21 |
| 0.7156 | 2000 | 0.207 |
| 0.8945 | 2500 | 0.198 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.3.1+cu121
- Accelerate: 0.32.1
- Datasets: 2.21.0
- 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",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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