---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:34969
- loss:CosineSimilarityLoss
base_model: BAAI/bge-base-en-v1.5
widget:
- source_sentence: Describe the role of the cell wall in plant cells.
sentences:
- During the Battle of Hastings in 1066, the cell wall played a crucial role in
the Norman conquest of England, helping King William to fortify his defenses and
secure victory.
- The relative atomic mass of oxygen is 16.
- Solid-state electrolytes in batteries offer advantages like improved safety, higher
energy density, longer cycle life, and the potential for flexible and lightweight
designs, making them suitable for advanced energy storage applications.
- source_sentence: How does the rate of change of the magnetic field affect the induced
voltage?
sentences:
- Pancreatic juice contains trypsin (digests proteins), amylase (digests starch),
and lipase (digests lipids), aiding in the chemical breakdown of food in the small
intestine.
- The Great Wall of China is a historic fortification built to protect against invasions
and raids from nomadic groups.
- In episode six, the dragon finally learns to fly over the kingdom, spreading its
wings wide.
- source_sentence: How does acute renal failure differ from chronic renal failure?
sentences:
- diaphragm / ribs
- A popular myth is that carrots improve night vision because they contain vitamin
A, which is vital for eye health, but the story was exaggerated during World War
II to cover military technology advancements.
- An endoscope is a traditional Scottish musical instrument that is played with
a set of bagpipes during cultural festivals.
- source_sentence: What is the molar mass of ammonium chloride (NH₄Cl)?
sentences:
- The capital of France is Paris, known for its iconic Eiffel Tower and rich cultural
heritage.
- The molar mass of NH₄Cl is 53.5 g/mol.
- 3. Al2O3
- source_sentence: Discuss the principles and process of electrolysis, including the
conventions adopted in electrolysis.
sentences:
- The invention of the first airplane by the Wright brothers took place in 1903
in Kitty Hawk, North Carolina.
- In the movie 'Inception', directed by Christopher Nolan, the plot revolves around
a skilled thief who is given a chance at redemption if he can successfully perform
inception by planting an idea into someone's subconscious.
- The development of artificial intelligence has significantly impacted the tech
industry, leading to advancements in machine learning and natural language processing.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- cosine_mcc
model-index:
- name: SentenceTransformer based on BAAI/bge-base-en-v1.5
results:
- task:
type: binary-classification
name: Binary Classification
dataset:
name: eval
type: eval
metrics:
- type: cosine_accuracy
value: 1.0
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.057119220495224
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 1.0
name: Cosine F1
- type: cosine_f1_threshold
value: 0.057119220495224
name: Cosine F1 Threshold
- type: cosine_precision
value: 1.0
name: Cosine Precision
- type: cosine_recall
value: 1.0
name: Cosine Recall
- type: cosine_ap
value: 1.0
name: Cosine Ap
- type: cosine_mcc
value: 1.0
name: Cosine Mcc
---
# SentenceTransformer based on BAAI/bge-base-en-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
### 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': 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("GaniduA/bge-finetuned-olscience")
# Run inference
sentences = [
'Discuss the principles and process of electrolysis, including the conventions adopted in electrolysis.',
'The development of artificial intelligence has significantly impacted the tech industry, leading to advancements in machine learning and natural language processing.',
"In the movie 'Inception', directed by Christopher Nolan, the plot revolves around a skilled thief who is given a chance at redemption if he can successfully perform inception by planting an idea into someone's subconscious.",
]
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]
```
## Evaluation
### Metrics
#### Binary Classification
* Dataset: `eval`
* Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:--------------------------|:--------|
| cosine_accuracy | 1.0 |
| cosine_accuracy_threshold | 0.0571 |
| cosine_f1 | 1.0 |
| cosine_f1_threshold | 0.0571 |
| cosine_precision | 1.0 |
| cosine_recall | 1.0 |
| **cosine_ap** | **1.0** |
| cosine_mcc | 1.0 |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 34,969 training samples
* Columns: sentence_0, sentence_1, and label
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | - min: 6 tokens
- mean: 17.43 tokens
- max: 209 tokens
| - min: 3 tokens
- mean: 25.94 tokens
- max: 335 tokens
| - min: 0.0
- mean: 0.25
- max: 1.0
|
* Samples:
| sentence_0 | sentence_1 | label |
|:------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| How does the reaction of zinc with copper sulfate demonstrate a single displacement reaction? | Julius Caesar crossed the Rubicon River in 49 BC, which led to a chain of events culminating in the Roman Civil War. | 0.0 |
| How do you investigate the effect of tightening a screw on the moment of force required to rotate a stick? | Explore the depths of the ocean with a team of deep-sea divers searching for mythical sea creatures and undiscovered shipwrecks. | 0.0 |
| Describe the operation of a photodiode in optical sensing. | A photodiode converts light into an electrical current by generating electron-hole pairs when exposed to light, used in optical sensing and communication applications. | 1.0 |
* 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`: 64
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 2
- `fp16`: True
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `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
- `torch_empty_cache_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`: 2
- `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`: 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}
- `tp_size`: 0
- `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`: round_robin