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---
base_model: Mihaiii/Venusaur
datasets:
- Mihaiii/qa-assistant-2
language:
- en
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:16011
- loss:CosineSimilarityLoss
widget:
- source_sentence: What impact does high-speed rail have on connectivity between cities?
sentences:
- Art supplies can be quite expensive, especially high-quality paints and brushes.
- High-speed rail can be a more comfortable and convenient mode of travel compared
to buses or cars.
- Engineers use a variety of methods to test the safety of autonomous vehicles,
including controlled track testing and public road trials.
- source_sentence: What is the best soil type for growing tomatoes?
sentences:
- Sandy loam soil is often considered ideal for growing tomatoes due to its good
drainage and nutrient-holding capacity.
- Socialist political systems are often contrasted with capitalist systems, which
prioritize private ownership and market-driven economies.
- The core principles of Sikhism include the belief in one God, the importance of
honest living, and the practice of selfless service.
- source_sentence: What are the three main types of rocks?
sentences:
- Mount Everest is the highest mountain in the world, located in the Himalayas.
- Archaeologists sometimes face challenges such as funding and access to advanced
technology, which can impact their ability to preserve findings.
- Some people are concerned about the ethical implications of genetic modification
in food production.
- source_sentence: How do vaccines help prevent diseases?
sentences:
- The theory also posits that during periods of economic downturn, increased government
spending can help stimulate demand and pull the economy out of recession.
- The Gurdwara is a place where Sikhs can participate in religious rituals and ceremonies,
such as weddings and naming ceremonies.
- The development of vaccines involves rigorous testing to ensure their safety and
efficacy before they are approved for public use.
- source_sentence: What are the social structures of ants?
sentences:
- The social hierarchy of ants is a complex system that ensures the survival and
efficiency of the colony.
- In a parliamentary system, the executive branch derives its legitimacy from and
is accountable to the legislature; the executive and legislative branches are
thus interconnected.
- Proper waste management and recycling can contribute to a more sustainable farming
operation.
model-index:
- name: SentenceTransformer based on Mihaiii/Venusaur
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts-dev
metrics:
- type: pearson_cosine
value: 0.826101669872389
name: Pearson Cosine
- type: spearman_cosine
value: 0.8277251878978443
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8199515763304537
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8225731321378551
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8214525375708358
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8236879484111633
name: Spearman Euclidean
- type: pearson_dot
value: 0.8037304918463798
name: Pearson Dot
- type: spearman_dot
value: 0.8082305683494836
name: Spearman Dot
- type: pearson_max
value: 0.826101669872389
name: Pearson Max
- type: spearman_max
value: 0.8277251878978443
name: Spearman Max
---
# SentenceTransformer based on Mihaiii/Venusaur
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Mihaiii/Venusaur](https://huggingface.co/Mihaiii/Venusaur) on the [Mihaiii/qa-assistant-2](https://huggingface.co/datasets/Mihaiii/qa-assistant-2) dataset. It maps sentences & paragraphs to a 384-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:** [Mihaiii/Venusaur](https://huggingface.co/Mihaiii/Venusaur) <!-- at revision 0dc817f0addbb7bab8feeeeaded538f9ffeb3419 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [Mihaiii/qa-assistant-2](https://huggingface.co/datasets/Mihaiii/qa-assistant-2)
- **Language:** en
<!-- - **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': 384, '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("sentence_transformers_model_id")
# Run inference
sentences = [
'What are the social structures of ants?',
'The social hierarchy of ants is a complex system that ensures the survival and efficiency of the colony.',
'In a parliamentary system, the executive branch derives its legitimacy from and is accountable to the legislature; the executive and legislative branches are thus interconnected.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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## 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 | 0.8261 |
| **spearman_cosine** | **0.8277** |
| pearson_manhattan | 0.82 |
| spearman_manhattan | 0.8226 |
| pearson_euclidean | 0.8215 |
| spearman_euclidean | 0.8237 |
| pearson_dot | 0.8037 |
| spearman_dot | 0.8082 |
| pearson_max | 0.8261 |
| spearman_max | 0.8277 |
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## Training Details
### Training Dataset
#### Mihaiii/qa-assistant-2
* Dataset: [Mihaiii/qa-assistant-2](https://huggingface.co/datasets/Mihaiii/qa-assistant-2) at [9650e69](https://huggingface.co/datasets/Mihaiii/qa-assistant-2/tree/9650e69ae0a030fa74a8706a20a168a613c43241)
* Size: 16,011 training samples
* Columns: <code>question</code>, <code>answer</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | question | answer | score |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 12.73 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 22.42 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 0.02</li><li>mean: 0.53</li><li>max: 1.0</li></ul> |
* Samples:
| question | answer | score |
|:-----------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
| <code>Can you describe the process of robot path planning?</code> | <code>Robots can be programmed to perform a variety of tasks, from simple repetitive actions to complex decision-making processes.</code> | <code>0.27999999999999997</code> |
| <code>Can humans live on Mars?</code> | <code>Mars is the fourth planet from the Sun and is often called the Red Planet due to its reddish appearance.</code> | <code>0.16</code> |
| <code>What are the key elements of composition in abstract art?</code> | <code>The history of abstract art dates back to the early 20th century, with pioneers like Wassily Kandinsky and Piet Mondrian.</code> | <code>0.36</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Evaluation Dataset
#### Mihaiii/qa-assistant-2
* Dataset: [Mihaiii/qa-assistant-2](https://huggingface.co/datasets/Mihaiii/qa-assistant-2) at [9650e69](https://huggingface.co/datasets/Mihaiii/qa-assistant-2/tree/9650e69ae0a030fa74a8706a20a168a613c43241)
* Size: 3,879 evaluation samples
* Columns: <code>question</code>, <code>answer</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | question | answer | score |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 7 tokens</li><li>mean: 12.71 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 22.63 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 0.02</li><li>mean: 0.53</li><li>max: 1.0</li></ul> |
* Samples:
| question | answer | score |
|:-------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
| <code>What is the concept of social stratification?</code> | <code>The study of social stratification involves examining the inequalities and divisions within a society.</code> | <code>0.6799999999999999</code> |
| <code>How does J.K. Rowling develop the character of Hermione Granger throughout the 'Harry Potter' series?</code> | <code>The 'Harry Potter' series consists of seven books, starting with 'Harry Potter and the Philosopher's Stone' and ending with 'Harry Potter and the Deathly Hallows'.</code> | <code>0.22000000000000003</code> |
| <code>What is the parliamentary system and how does it function?</code> | <code>In a parliamentary system, the government can be dissolved by a vote of no confidence, which can lead to new elections.</code> | <code>0.6799999999999999</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1
#### 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`: 4
- `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`: 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`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine |
|:------:|:----:|:-------------:|:------:|:-----------------------:|
| 0.0999 | 100 | 0.0593 | 0.0540 | 0.5848 |
| 0.1998 | 200 | 0.05 | 0.0463 | 0.6618 |
| 0.2997 | 300 | 0.044 | 0.0418 | 0.7102 |
| 0.3996 | 400 | 0.0413 | 0.0385 | 0.7390 |
| 0.4995 | 500 | 0.0377 | 0.0349 | 0.7707 |
| 0.5994 | 600 | 0.034 | 0.0333 | 0.7770 |
| 0.6993 | 700 | 0.0344 | 0.0321 | 0.7879 |
| 0.7992 | 800 | 0.0324 | 0.0311 | 0.7927 |
| 0.8991 | 900 | 0.0334 | 0.0302 | 0.8005 |
| 0.9990 | 1000 | 0.0304 | 0.0305 | 0.8023 |
| 1.0989 | 1100 | 0.0261 | 0.0306 | 0.8072 |
| 1.1988 | 1200 | 0.0267 | 0.0292 | 0.8104 |
| 1.2987 | 1300 | 0.0244 | 0.0287 | 0.8110 |
| 1.3986 | 1400 | 0.0272 | 0.0294 | 0.8098 |
| 1.4985 | 1500 | 0.0241 | 0.0281 | 0.8135 |
| 1.5984 | 1600 | 0.0253 | 0.0282 | 0.8143 |
| 1.6983 | 1700 | 0.0245 | 0.0276 | 0.8169 |
| 1.7982 | 1800 | 0.025 | 0.0274 | 0.8182 |
| 1.8981 | 1900 | 0.0236 | 0.0273 | 0.8193 |
| 1.9980 | 2000 | 0.0236 | 0.0269 | 0.8218 |
| 2.0979 | 2100 | 0.0215 | 0.0278 | 0.8213 |
| 2.1978 | 2200 | 0.0216 | 0.0269 | 0.8226 |
| 2.2977 | 2300 | 0.0205 | 0.0276 | 0.8207 |
| 2.3976 | 2400 | 0.0181 | 0.0273 | 0.8202 |
| 2.4975 | 2500 | 0.0197 | 0.0267 | 0.8228 |
| 2.5974 | 2600 | 0.02 | 0.0267 | 0.8238 |
| 2.6973 | 2700 | 0.0203 | 0.0263 | 0.8258 |
| 2.7972 | 2800 | 0.0184 | 0.0263 | 0.8264 |
| 2.8971 | 2900 | 0.0201 | 0.0269 | 0.8243 |
| 2.9970 | 3000 | 0.0196 | 0.0263 | 0.8251 |
| 3.0969 | 3100 | 0.0168 | 0.0264 | 0.8250 |
| 3.1968 | 3200 | 0.0176 | 0.0263 | 0.8267 |
| 3.2967 | 3300 | 0.0168 | 0.0263 | 0.8270 |
| 3.3966 | 3400 | 0.017 | 0.0260 | 0.8277 |
| 3.4965 | 3500 | 0.0164 | 0.0261 | 0.8273 |
| 3.5964 | 3600 | 0.0172 | 0.0259 | 0.8280 |
| 3.6963 | 3700 | 0.0168 | 0.0260 | 0.8274 |
| 3.7962 | 3800 | 0.0176 | 0.0262 | 0.8279 |
| 3.8961 | 3900 | 0.0182 | 0.0261 | 0.8278 |
| 3.9960 | 4000 | 0.0174 | 0.0260 | 0.8277 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.0.1+cu118
- Accelerate: 0.31.0
- Datasets: 2.20.0
- 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",
}
```
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