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---
base_model: sentence-transformers/all-MiniLM-L12-v2
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:100000
- loss:CosineSimilarityLoss
widget:
- source_sentence: Face off with a ref mid-hockey game in an arena.
sentences:
- Nobody is playing
- A mustached man in a patterned shirt watches a boat painted blue and orange.
- Two adults makes calls on there cell phones during there lunch breaks.
- source_sentence: A group of people, one holding a yellow and blue umbrella, are
standing at the top of some stairs.
sentences:
- One person wields an umbrella.
- A girl is on the beach.
- A man is on his couch.
- source_sentence: A man waiting for the results of the machine after doing an experiment
in his laboratory.
sentences:
- There is a man playing an instrument while running
- A man in a lab waits to get more information about his experiment.
- The graffiti artists admire their work.
- source_sentence: People in a tent shelter near the bottom of stairs.
sentences:
- A boy has fallen asleep during dinner.
- Three men address a crowd.
- People are in a makeshift shelter at the foot of a staircase.
- source_sentence: A female researcher looking through a microscope.
sentences:
- A man misses the rope and falls
- A small girl is playing video games
- A woman is researching with a microscope.
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L12-v2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: snli dev
type: snli-dev
metrics:
- type: pearson_cosine
value: 0.48994508338253345
name: Pearson Cosine
- type: spearman_cosine
value: 0.4778683474663533
name: Spearman Cosine
- type: pearson_manhattan
value: 0.46917600703738915
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.47754796729416876
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.46924620767742137
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.4778683474663533
name: Spearman Euclidean
- type: pearson_dot
value: 0.48994508631435785
name: Pearson Dot
- type: spearman_dot
value: 0.4778683472855999
name: Spearman Dot
- type: pearson_max
value: 0.48994508631435785
name: Pearson Max
- type: spearman_max
value: 0.4778683474663533
name: Spearman Max
---
# SentenceTransformer based on sentence-transformers/all-MiniLM-L12-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-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-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) <!-- at revision 30ce63ae64e71b9199b3d2eae9de99f64a26eedc -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 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': 128, '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("Nessrine9/finetuned2-snli-MiniLM-L12-v2")
# Run inference
sentences = [
'A female researcher looking through a microscope.',
'A woman is researching with a microscope.',
'A small girl is playing video games',
]
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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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `snli-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:-------------------|:-----------|
| pearson_cosine | 0.4899 |
| spearman_cosine | 0.4779 |
| pearson_manhattan | 0.4692 |
| spearman_manhattan | 0.4775 |
| pearson_euclidean | 0.4692 |
| spearman_euclidean | 0.4779 |
| pearson_dot | 0.4899 |
| spearman_dot | 0.4779 |
| pearson_max | 0.4899 |
| **spearman_max** | **0.4779** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 100,000 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 7 tokens</li><li>mean: 16.32 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.46 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:---------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------|:-----------------|
| <code>A man wearing jeans and a t-shirt plays guitar for a smiling woman and child as they sit on a staircase near red and orange balloons.</code> | <code>A man is in jail.</code> | <code>1.0</code> |
| <code>A boy wearing blue short standing on the traffic signal pole.</code> | <code>The boy is carrying his school books.</code> | <code>0.5</code> |
| <code>Several people on a busy street or perhaps at a fair.</code> | <code>They are walkng.</code> | <code>0.5</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
- `fp16`: True
- `multi_dataset_batch_sampler`: round_robin
#### 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
- `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`: 4
- `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}
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
| Epoch | Step | Training Loss | snli-dev_spearman_max |
|:------:|:-----:|:-------------:|:---------------------:|
| 0.08 | 500 | 0.1832 | 0.3114 |
| 0.16 | 1000 | 0.1489 | 0.3518 |
| 0.24 | 1500 | 0.1468 | 0.3697 |
| 0.32 | 2000 | 0.1411 | 0.3723 |
| 0.4 | 2500 | 0.14 | 0.4062 |
| 0.48 | 3000 | 0.1366 | 0.3923 |
| 0.56 | 3500 | 0.1379 | 0.4143 |
| 0.64 | 4000 | 0.1357 | 0.3928 |
| 0.72 | 4500 | 0.1331 | 0.4067 |
| 0.8 | 5000 | 0.1338 | 0.4293 |
| 0.88 | 5500 | 0.1294 | 0.4183 |
| 0.96 | 6000 | 0.1305 | 0.4402 |
| 1.0 | 6250 | - | 0.4454 |
| 1.04 | 6500 | 0.1303 | 0.4408 |
| 1.12 | 7000 | 0.1275 | 0.4416 |
| 1.2 | 7500 | 0.1285 | 0.4287 |
| 1.28 | 8000 | 0.125 | 0.4404 |
| 1.3600 | 8500 | 0.1253 | 0.4408 |
| 1.44 | 9000 | 0.1246 | 0.4293 |
| 1.52 | 9500 | 0.126 | 0.4535 |
| 1.6 | 10000 | 0.1257 | 0.4455 |
| 1.6800 | 10500 | 0.1264 | 0.4520 |
| 1.76 | 11000 | 0.1248 | 0.4526 |
| 1.8400 | 11500 | 0.1208 | 0.4631 |
| 1.92 | 12000 | 0.1236 | 0.4635 |
| 2.0 | 12500 | 0.1239 | 0.4573 |
| 2.08 | 13000 | 0.1209 | 0.4569 |
| 2.16 | 13500 | 0.1194 | 0.4642 |
| 2.24 | 14000 | 0.1206 | 0.4539 |
| 2.32 | 14500 | 0.117 | 0.4633 |
| 2.4 | 15000 | 0.1171 | 0.4657 |
| 2.48 | 15500 | 0.1181 | 0.4633 |
| 2.56 | 16000 | 0.1197 | 0.4552 |
| 2.64 | 16500 | 0.1182 | 0.4670 |
| 2.7200 | 17000 | 0.1155 | 0.4684 |
| 2.8 | 17500 | 0.1171 | 0.4640 |
| 2.88 | 18000 | 0.1139 | 0.4715 |
| 2.96 | 18500 | 0.1164 | 0.4769 |
| 3.0 | 18750 | - | 0.4709 |
| 3.04 | 19000 | 0.1151 | 0.4704 |
| 3.12 | 19500 | 0.1144 | 0.4759 |
| 3.2 | 20000 | 0.1121 | 0.4795 |
| 3.2800 | 20500 | 0.1104 | 0.4697 |
| 3.36 | 21000 | 0.1127 | 0.4763 |
| 3.44 | 21500 | 0.1115 | 0.4742 |
| 3.52 | 22000 | 0.1126 | 0.4697 |
| 3.6 | 22500 | 0.1123 | 0.4735 |
| 3.68 | 23000 | 0.1132 | 0.4750 |
| 3.76 | 23500 | 0.1127 | 0.4743 |
| 3.84 | 24000 | 0.1086 | 0.4752 |
| 3.92 | 24500 | 0.1107 | 0.4781 |
| 4.0 | 25000 | 0.1114 | 0.4779 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.2.1
- Transformers: 4.44.2
- PyTorch: 2.5.0+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.2
- 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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