---
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: NIPA personal income includes pension contributions by employers
in the year income is earned , and benefits paid at retirement are not a component
of NIPA income .
sentences:
- While not the only makeup of income , NIPA is one of the more well known income
distinctions .
- Les temples de karnak et de Louxor ont été démolis pour faire place à des projets
de construction en Cisjordanie .
- Les restaurants sont tenus à des règles strictes pour contenir leur odeur .
- source_sentence: right right you know the one that 's one reason we bought a house
here in Plano we were hoping you know well the school district 's gonna be good
you know for resale value and so on and so forth but
sentences:
- We moved to Plano because we thought the school district was good .
- These and those .
- L' obsession a suscité une suggestion que tous étaient des boucs émissaires de
la guerre .
- source_sentence: Dans l' homme invisible , le talentueux dixième narrateur doit
surmonter non seulement les différentes idéologies qui lui sont présentées comme
masques ou subversions d' identité , mais aussi les différents rôles et prescriptions
pour le leadership que sa propre race lui souhaite de réaliser .
sentences:
- '" We ''re too uptight now ! " Said Tommy'
- Le talentueux dixième narrateur doit surmonter les idéologies .
- Saddam is not taking advantage of the current Arab love towards the United States
- source_sentence: Les lacunes trouvées au cours de la surveillance en cours ou au
moyen d' évaluations distinctes devraient être communiquées à l' individu responsable
de la fonction et à au moins un niveau de gestion au-dessus de cet individu .
sentences:
- L' économie diminuera également si les conditions du marché changent .
- The Watergate comparison wasn 't just for Democratic bashing .
- Il n' y a pas lieu de signaler les lacunes .
- source_sentence: it looks fertile and it it um i mean it rains enough they have
the climate and the rain and if not it 's like i 've been to Saint Thomas and
it just starts from the ocean up
sentences:
- Il n' a jamais triché .
- They don 't know how to do it .
- They have the rain and the climate so I imagine the lands would be fertile .
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: 0.3725313255221131
name: Pearson Cosine
- type: spearman_cosine
value: 0.3729470854776107
name: Spearman Cosine
- type: pearson_manhattan
value: 0.3650227128515394
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.37250760289182383
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.36567325497563746
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.37294699995093694
name: Spearman Euclidean
- type: pearson_dot
value: 0.3725313190046259
name: Pearson Dot
- type: spearman_dot
value: 0.3729474276296007
name: Spearman Dot
- type: pearson_max
value: 0.3725313255221131
name: Pearson Max
- type: spearman_max
value: 0.3729474276296007
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)
- **Maximum Sequence Length:** 128 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': 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("cherifkhalifah/finetuned-snli-MiniLM-L12-v2-100k-en-fr")
# Run inference
sentences = [
"it looks fertile and it it um i mean it rains enough they have the climate and the rain and if not it 's like i 've been to Saint Thomas and it just starts from the ocean up",
'They have the rain and the climate so I imagine the lands would be fertile .',
"They don 't know how to do it .",
]
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: `snli-dev`
* Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:-------------------|:-----------|
| pearson_cosine | 0.3725 |
| spearman_cosine | 0.3729 |
| pearson_manhattan | 0.365 |
| spearman_manhattan | 0.3725 |
| pearson_euclidean | 0.3657 |
| spearman_euclidean | 0.3729 |
| pearson_dot | 0.3725 |
| spearman_dot | 0.3729 |
| pearson_max | 0.3725 |
| **spearman_max** | **0.3729** |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 100,000 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: 5 tokens
- mean: 35.27 tokens
- max: 128 tokens
| - min: 5 tokens
- mean: 18.46 tokens
- max: 66 tokens
| - min: 0.0
- mean: 0.5
- max: 1.0
|
* Samples:
| sentence_0 | sentence_1 | label |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------|:-----------------|
| Natalia M' a regardé . | Natalia a regardé et attend que je lui donne l' épée . | 0.5 |
| And he sounded sincere . | He sounded sincere.He was sounding sincere in his words . | 0.0 |
| There 's a small zoo area where you can see snakes , lizards , birds of prey , wolves , hyenas , foxes , and various desert cats , including cheetahs and leopards . | The zoo is home to some endangered desert animals . | 0.5 |
* 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
- `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`: 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