---
base_model: sentence-transformers/all-MiniLM-L6-v2
datasets: []
language: []
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- dot_accuracy
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_ap
- manhattan_accuracy
- manhattan_accuracy_threshold
- manhattan_f1
- manhattan_f1_threshold
- manhattan_precision
- manhattan_recall
- manhattan_ap
- euclidean_accuracy
- euclidean_accuracy_threshold
- euclidean_f1
- euclidean_f1_threshold
- euclidean_precision
- euclidean_recall
- euclidean_ap
- max_accuracy
- max_accuracy_threshold
- max_f1
- max_f1_threshold
- max_precision
- max_recall
- max_ap
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:560
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Let's search inside
sentences:
- Stuffed animal
- Let's look inside
- What is worse?
- source_sentence: I want a torch
sentences:
- What do you think of Spike
- Actually I want a torch
- Why candle?
- source_sentence: Magic trace
sentences:
- A sword.
- ' Why is he so tiny?'
- 'The flower is changed into flower. '
- source_sentence: Did you use illusion?
sentences:
- Do you use illusion?
- You are a cat?
- It's Toby
- source_sentence: Do you see your scarf in the watering can?
sentences:
- What is the Weeping Tree?
- Are these your footprints?
- Magic user
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
results:
- task:
type: binary-classification
name: Binary Classification
dataset:
name: custom arc semantics data
type: custom-arc-semantics-data
metrics:
- type: cosine_accuracy
value: 0.85
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.49632835388183594
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.8727272727272727
name: Cosine F1
- type: cosine_f1_threshold
value: 0.48691314458847046
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.8888888888888888
name: Cosine Precision
- type: cosine_recall
value: 0.8571428571428571
name: Cosine Recall
- type: cosine_ap
value: 0.927175101411552
name: Cosine Ap
- type: dot_accuracy
value: 0.85
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 0.4963283836841583
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.8727272727272727
name: Dot F1
- type: dot_f1_threshold
value: 0.48691320419311523
name: Dot F1 Threshold
- type: dot_precision
value: 0.8888888888888888
name: Dot Precision
- type: dot_recall
value: 0.8571428571428571
name: Dot Recall
- type: dot_ap
value: 0.927175101411552
name: Dot Ap
- type: manhattan_accuracy
value: 0.8428571428571429
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 15.624195098876953
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.8681318681318683
name: Manhattan F1
- type: manhattan_f1_threshold
value: 18.23479461669922
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.8061224489795918
name: Manhattan Precision
- type: manhattan_recall
value: 0.9404761904761905
name: Manhattan Recall
- type: manhattan_ap
value: 0.9264219833665228
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.85
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 1.00364351272583
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.8727272727272727
name: Euclidean F1
- type: euclidean_f1_threshold
value: 1.0129987001419067
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.8888888888888888
name: Euclidean Precision
- type: euclidean_recall
value: 0.8571428571428571
name: Euclidean Recall
- type: euclidean_ap
value: 0.927175101411552
name: Euclidean Ap
- type: max_accuracy
value: 0.85
name: Max Accuracy
- type: max_accuracy_threshold
value: 15.624195098876953
name: Max Accuracy Threshold
- type: max_f1
value: 0.8727272727272727
name: Max F1
- type: max_f1_threshold
value: 18.23479461669922
name: Max F1 Threshold
- type: max_precision
value: 0.8888888888888888
name: Max Precision
- type: max_recall
value: 0.9404761904761905
name: Max Recall
- type: max_ap
value: 0.927175101411552
name: Max Ap
---
# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). 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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 tokens
- **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': 256, '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})
(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("LeoChiuu/all-MiniLM-L6-v2")
# Run inference
sentences = [
'Do you see your scarf in the watering can?',
'Are these your footprints?',
'Magic user',
]
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
#### Binary Classification
* Dataset: `custom-arc-semantics-data`
* Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.85 |
| cosine_accuracy_threshold | 0.4963 |
| cosine_f1 | 0.8727 |
| cosine_f1_threshold | 0.4869 |
| cosine_precision | 0.8889 |
| cosine_recall | 0.8571 |
| cosine_ap | 0.9272 |
| dot_accuracy | 0.85 |
| dot_accuracy_threshold | 0.4963 |
| dot_f1 | 0.8727 |
| dot_f1_threshold | 0.4869 |
| dot_precision | 0.8889 |
| dot_recall | 0.8571 |
| dot_ap | 0.9272 |
| manhattan_accuracy | 0.8429 |
| manhattan_accuracy_threshold | 15.6242 |
| manhattan_f1 | 0.8681 |
| manhattan_f1_threshold | 18.2348 |
| manhattan_precision | 0.8061 |
| manhattan_recall | 0.9405 |
| manhattan_ap | 0.9264 |
| euclidean_accuracy | 0.85 |
| euclidean_accuracy_threshold | 1.0036 |
| euclidean_f1 | 0.8727 |
| euclidean_f1_threshold | 1.013 |
| euclidean_precision | 0.8889 |
| euclidean_recall | 0.8571 |
| euclidean_ap | 0.9272 |
| max_accuracy | 0.85 |
| max_accuracy_threshold | 15.6242 |
| max_f1 | 0.8727 |
| max_f1_threshold | 18.2348 |
| max_precision | 0.8889 |
| max_recall | 0.9405 |
| **max_ap** | **0.9272** |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 560 training samples
* Columns: text1, text2, and label
* Approximate statistics based on the first 1000 samples:
| | text1 | text2 | label |
|:--------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | - min: 3 tokens
- mean: 7.2 tokens
- max: 18 tokens
| - min: 3 tokens
- mean: 7.26 tokens
- max: 18 tokens
| |
* Samples:
| text1 | text2 | label |
|:-----------------------------------------------------|:--------------------------------------------------------------------------|:---------------|
| When it was dinner | Dinner time | 1 |
| Did you cook chicken noodle last night? | Did you make chicken noodle for dinner? | 1 |
| Someone who can change item | Someone who uses magic that turns something into something. | 1 |
* Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 140 evaluation samples
* Columns: text1, text2, and label
* Approximate statistics based on the first 1000 samples:
| | text1 | text2 | label |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | - min: 3 tokens
- mean: 6.99 tokens
- max: 18 tokens
| - min: 3 tokens
- mean: 7.29 tokens
- max: 18 tokens
| |
* Samples:
| text1 | text2 | label |
|:-----------------------------------------|:-----------------------------------------|:---------------|
| Let's check inside | Let's search inside | 1 |
| Sohpie, are you okay? | Sophie Are you pressured? | 0 |
| This wine glass is related. | This sword looks important. | 0 |
* Loss: [MultipleNegativesRankingLoss](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
- `eval_strategy`: epoch
- `learning_rate`: 2e-05
- `num_train_epochs`: 13
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `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`: 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`: 13
- `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
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional