---
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)
- **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
### 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]
```
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-dev`
* Evaluated with [EmbeddingSimilarityEvaluator](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 |
## 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: question, answer, and score
* Approximate statistics based on the first 1000 samples:
| | question | answer | score |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------|
| type | string | string | float |
| details | - min: 6 tokens
- mean: 12.73 tokens
- max: 27 tokens
| - min: 10 tokens
- mean: 22.42 tokens
- max: 65 tokens
| - min: 0.02
- mean: 0.53
- max: 1.0
|
* Samples:
| question | answer | score |
|:-----------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
| Can you describe the process of robot path planning? | Robots can be programmed to perform a variety of tasks, from simple repetitive actions to complex decision-making processes. | 0.27999999999999997 |
| Can humans live on Mars? | Mars is the fourth planet from the Sun and is often called the Red Planet due to its reddish appearance. | 0.16 |
| What are the key elements of composition in abstract art? | The history of abstract art dates back to the early 20th century, with pioneers like Wassily Kandinsky and Piet Mondrian. | 0.36 |
* Loss: [CosineSimilarityLoss](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: question, answer, and score
* Approximate statistics based on the first 1000 samples:
| | question | answer | score |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------|
| type | string | string | float |
| details | - min: 7 tokens
- mean: 12.71 tokens
- max: 31 tokens
| - min: 10 tokens
- mean: 22.63 tokens
- max: 51 tokens
| - min: 0.02
- mean: 0.53
- max: 1.0
|
* Samples:
| question | answer | score |
|:-------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
| What is the concept of social stratification? | The study of social stratification involves examining the inequalities and divisions within a society. | 0.6799999999999999 |
| How does J.K. Rowling develop the character of Hermione Granger throughout the 'Harry Potter' series? | 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'. | 0.22000000000000003 |
| What is the parliamentary system and how does it function? | In a parliamentary system, the government can be dissolved by a vote of no confidence, which can lead to new elections. | 0.6799999999999999 |
* Loss: [CosineSimilarityLoss](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
Click to expand
- `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