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---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:77376
- loss:CosineSimilarityLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
datasets: []
widget:
- source_sentence: He has published several books on nutrition, trace metals but not
biochemistry imbalances.
sentences:
- This in turn can help in effective communication between healthcare providers
and their patients.
- He has written several books on nutrition, trace metals, and biochemistry imbalances.
- One of the most boring movies I have ever seen.
- source_sentence: She was denied the 2011 NSK Neustadt Prize for Children's Literature.
sentences:
- She was the recipient of the 2011 NSK Neustadt Prize for Children's Literature.
- The ancient woodland at Dickshills is also located here.
- An element (such as a tree) that contributes to evapotranspiration can be called
an evapotranspirator.
- source_sentence: Viking, after the resemblance the pitchers bear to the prow of
a Viking ship.
sentences:
- Viking, after the striking difference the pitchers bear to the prow of a Viking
ship.
- Honshu is formed from the island arcs.
- For instance, even alcohol consumption by a pregnant woman is unable to lead to
fetal alcohol syndrome.
- source_sentence: Logging has not been undertake near the headwaters of the creek.
sentences:
- Then I had to continue pairing it periodically since it somehow kept dropping.
- That's fair, Nance.
- Logging has been done near the headwaters of the creek.
- source_sentence: He published a history of Cornwall, New York in 1873.
sentences:
- He failed to publish a history of Cornwall, New York in 1873.
- Salafis assert that reliance on taqlid has led to Islam 's decline.
- 'Lot of holes in the plot: there''s nothing about how he became the emperor; nothing
about where he spend 20 years between his childhood and mature age.'
pipeline_tag: sentence-similarity
---
# 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) <!-- at revision 8b3219a92973c328a8e22fadcfa821b5dc75636a -->
- **Maximum Sequence Length:** 256 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': 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-negations")
# Run inference
sentences = [
'He published a history of Cornwall, New York in 1873.',
'He failed to publish a history of Cornwall, New York in 1873.',
"Salafis assert that reliance on taqlid has led to Islam 's decline.",
]
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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You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 77,376 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 | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 16.2 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.32 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>0: ~53.20%</li><li>1: ~46.80%</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:--------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------|
| <code>The situation in Yemen was already much better than it was in Bahrain.</code> | <code>The situation in Yemen was not much better than Bahrain.</code> | <code>0</code> |
| <code>She was a member of the Gamma Theta Upsilon honour society of geography.</code> | <code>She was denied membership of the Gamma Theta Upsilon honour society of mathematics.</code> | <code>0</code> |
| <code>Which aren't small and not worth the price.</code> | <code>Which are small and not worth the price.</code> | <code>0</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
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 10
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `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
- `num_train_epochs`: 10
- `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
- `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, '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_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:-----:|:-------------:|
| 0.1034 | 500 | 0.3382 |
| 0.2068 | 1000 | 0.2112 |
| 0.3102 | 1500 | 0.1649 |
| 0.4136 | 2000 | 0.1454 |
| 0.5170 | 2500 | 0.1244 |
| 0.6203 | 3000 | 0.1081 |
| 0.7237 | 3500 | 0.0962 |
| 0.8271 | 4000 | 0.0924 |
| 0.9305 | 4500 | 0.0852 |
| 1.0339 | 5000 | 0.0812 |
| 1.1373 | 5500 | 0.0833 |
| 1.2407 | 6000 | 0.0736 |
| 1.3441 | 6500 | 0.0756 |
| 1.4475 | 7000 | 0.0665 |
| 1.5509 | 7500 | 0.0661 |
| 1.6543 | 8000 | 0.0625 |
| 1.7577 | 8500 | 0.0621 |
| 1.8610 | 9000 | 0.0593 |
| 1.9644 | 9500 | 0.054 |
| 2.0678 | 10000 | 0.0569 |
| 2.1712 | 10500 | 0.0566 |
| 2.2746 | 11000 | 0.0502 |
| 2.3780 | 11500 | 0.0516 |
| 2.4814 | 12000 | 0.0455 |
| 2.5848 | 12500 | 0.0454 |
| 2.6882 | 13000 | 0.0424 |
| 2.7916 | 13500 | 0.044 |
| 2.8950 | 14000 | 0.0376 |
| 2.9983 | 14500 | 0.0386 |
| 3.1017 | 15000 | 0.0392 |
| 3.2051 | 15500 | 0.0344 |
| 3.3085 | 16000 | 0.0348 |
| 3.4119 | 16500 | 0.0343 |
| 3.5153 | 17000 | 0.0322 |
| 3.6187 | 17500 | 0.0324 |
| 3.7221 | 18000 | 0.0278 |
| 3.8255 | 18500 | 0.0294 |
| 3.9289 | 19000 | 0.0292 |
| 4.0323 | 19500 | 0.0276 |
| 4.1356 | 20000 | 0.0285 |
| 4.2390 | 20500 | 0.026 |
| 4.3424 | 21000 | 0.0271 |
| 4.4458 | 21500 | 0.0248 |
| 4.5492 | 22000 | 0.0245 |
| 4.6526 | 22500 | 0.0253 |
| 4.7560 | 23000 | 0.022 |
| 4.8594 | 23500 | 0.0219 |
| 4.9628 | 24000 | 0.0207 |
| 5.0662 | 24500 | 0.0212 |
| 5.1696 | 25000 | 0.0218 |
| 5.2730 | 25500 | 0.0192 |
| 5.3763 | 26000 | 0.0198 |
| 5.4797 | 26500 | 0.0183 |
| 5.5831 | 27000 | 0.02 |
| 5.6865 | 27500 | 0.0176 |
| 5.7899 | 28000 | 0.0184 |
| 5.8933 | 28500 | 0.0157 |
| 5.9967 | 29000 | 0.0175 |
| 6.1001 | 29500 | 0.0175 |
| 6.2035 | 30000 | 0.0163 |
| 6.3069 | 30500 | 0.0173 |
| 6.4103 | 31000 | 0.0165 |
| 6.5136 | 31500 | 0.0152 |
| 6.6170 | 32000 | 0.0155 |
| 6.7204 | 32500 | 0.0132 |
| 6.8238 | 33000 | 0.0147 |
| 6.9272 | 33500 | 0.0145 |
| 7.0306 | 34000 | 0.014 |
| 7.1340 | 34500 | 0.0147 |
| 7.2374 | 35000 | 0.0126 |
| 7.3408 | 35500 | 0.0141 |
| 7.4442 | 36000 | 0.0127 |
| 7.5476 | 36500 | 0.0132 |
| 7.6510 | 37000 | 0.0125 |
| 7.7543 | 37500 | 0.0111 |
| 7.8577 | 38000 | 0.011 |
| 7.9611 | 38500 | 0.0125 |
| 8.0645 | 39000 | 0.0128 |
| 8.1679 | 39500 | 0.013 |
| 8.2713 | 40000 | 0.0115 |
| 8.3747 | 40500 | 0.0111 |
| 8.4781 | 41000 | 0.0108 |
| 8.5815 | 41500 | 0.012 |
| 8.6849 | 42000 | 0.0108 |
| 8.7883 | 42500 | 0.0105 |
| 8.8916 | 43000 | 0.0092 |
| 8.9950 | 43500 | 0.0115 |
| 9.0984 | 44000 | 0.0112 |
| 9.2018 | 44500 | 0.0096 |
| 9.3052 | 45000 | 0.0106 |
| 9.4086 | 45500 | 0.011 |
| 9.5120 | 46000 | 0.01 |
| 9.6154 | 46500 | 0.011 |
| 9.7188 | 47000 | 0.0097 |
| 9.8222 | 47500 | 0.0096 |
| 9.9256 | 48000 | 0.0102 |
### Framework Versions
- Python: 3.11.9
- Sentence Transformers: 3.0.1
- Transformers: 4.40.2
- PyTorch: 2.3.0+cpu
- Accelerate: 0.32.1
- Datasets: 2.19.1
- 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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