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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:32351
- loss:TripletLoss
base_model: sentence-transformers/all-mpnet-base-v2
widget:
- source_sentence: Genetic conditions that cause nutritional deficiencies can prevent
a person from removing meat from their diet.
sentences:
- Ante un estado que no quiere hablar del tema, para Cataluña, solo es posible seguir
su propio camino por otras vías.
- Retinol deficiency is a genetically pre-disposed condition that prevents conversion
beta-carotene to Vitamin A \(retinol\) in humans. Since plants have no retinol
\(only beta-carotene\), humans with this condition cannot have a vegan diet, only
one with animal products.
- People with hemochromatosis \(a genetic condition\) can benefit greatly from a
vegan diet, due to the lower absorbing non-heme iron in plants \(compared to heme
iron in meat\).
- source_sentence: 'The definition of veganism is: "A way of living which seeks to
exclude, as far as is possible and practicable, all forms of exploitation of,
and cruelty to, animals for food, clothing or any other purpose." In the \(unlikely\)
case of survival or health concerns, the "as far as possible and practicable"
clause makes it possible for such persons to be considered vegan as they would
have no alternative options.'
sentences:
- Veganism is not solely about diet. A person can still choose to live in accordance
with vegan values, such as by avoiding animal circuses and leather/fur products.
- It's easier to regulate established companies in a legal market than it is in
the black market. Any issue would be with bad regulations not legalization.
- That definition is too vague. There are different definitions of veganism, many
of which are not compatible with using animals in any circumstances. In a way
we are all vegan depending on how easy you believe it is to reach all the necessary
nutrition in your city harming as few animals as possible.
- source_sentence: Adding coding to the school curriculum means that something else
must be left out.
sentences:
- Coding skills are much needed in today's job market.
- Cataluña saldría de la UE con efectos económicos desastrosos.
- Teaching coding effectively is impossible unless teachers are trained appropriately
first.
- source_sentence: Animals have innate, individual rights, which are taken away when
they are killed or made to suffer.
sentences:
- Animals have a desire to live.
- Uno de los ejemplos más claros es la falta de inversión reiterada al Corredor
Mediterráneo \(Algeciras-Valencia-Barcelona-Francia\), prioritario para la UE
y Catalunya, pero relegado a algo residual por el estado Español.
- A vegan society would equate humans rights with animal rights, which would make
society worse off overall.
- source_sentence: The sorts of people likely to lash out against affirmative action
policies probably already hold negative views towards racial minorities.
sentences:
- The Far Right movement sees the inequality affirmative action addresses not as
a problem to be solved, but as an outcome to be desired.
- There are plenty of people who hold a positive view towards racial minorities
and still oppose affirmative action.
- Research has shown that college degrees have less economic utility for people
from low socio-economic backgrounds.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
results:
- task:
type: triplet
name: Triplet
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy
value: 0.9264069199562073
name: Cosine Accuracy
- type: cosine_accuracy
value: 0.9161931872367859
name: Cosine Accuracy
---
# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 9a3225965996d404b775526de6dbfe85d3368642 -->
- **Maximum Sequence Length:** 384 tokens
- **Output Dimensionality:** 768 dimensions
- **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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(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})
(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("sentence_transformers_model_id")
# Run inference
sentences = [
'The sorts of people likely to lash out against affirmative action policies probably already hold negative views towards racial minorities.',
'The Far Right movement sees the inequality affirmative action addresses not as a problem to be solved, but as an outcome to be desired.',
'There are plenty of people who hold a positive view towards racial minorities and still oppose affirmative action.',
]
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]
```
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### Direct Usage (Transformers)
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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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### Out-of-Scope Use
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## Evaluation
### Metrics
#### Triplet
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.9264** |
#### Triplet
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.9162** |
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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: 32,351 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 30.94 tokens</li><li>max: 160 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 40.8 tokens</li><li>max: 180 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 44.95 tokens</li><li>max: 162 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:----------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>La soberanía y la decisión sobre la unidad de España residen en el conjunto de España.</code> | <code>Apostar por un proceso de secesión es ir en contra de la globalización, la corriente histórica que vivimos.</code> | <code>Los tratados internacionales \(incluido el Tratado de La Unión Europea\) no serían aplicables a Cataluña como estado independiente, por lo que su permanencia en Europa podría verse interrumpida.</code> |
| <code>La soberanía y la decisión sobre la unidad de España residen en el conjunto de España.</code> | <code>Para sentar un precedente en conflictos de autodeterminación en el mundo.</code> | <code>La independencia de Cataluña afectaría negativamente a la economía de España.</code> |
| <code>La soberanía y la decisión sobre la unidad de España residen en el conjunto de España.</code> | <code>Para terminar con el trato injusto que recibe Cataluña al ser parte de España.</code> | <code>Por definición, cualquier nacionalismo es malo ya que crea divisiones artificiales y es fuente de conflictos.</code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"distance_metric": "TripletDistanceMetric.COSINE",
"triplet_margin": 0.3
}
```
### Training Hyperparameters
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `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`: 5e-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`: 3.0
- `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`: 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, '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`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | cosine_accuracy |
|:------:|:-----:|:-------------:|:---------------:|
| 0.1236 | 500 | 0.1872 | - |
| 0.2473 | 1000 | 0.1954 | - |
| 0.3709 | 1500 | 0.1854 | - |
| 0.4946 | 2000 | 0.1891 | - |
| 0.6182 | 2500 | 0.181 | - |
| 0.7418 | 3000 | 0.1794 | - |
| 0.8655 | 3500 | 0.1815 | - |
| 0.9891 | 4000 | 0.1736 | - |
| 1.1128 | 4500 | 0.1342 | - |
| 1.2364 | 5000 | 0.1297 | - |
| 1.3600 | 5500 | 0.1318 | - |
| 1.4837 | 6000 | 0.1255 | - |
| 1.6073 | 6500 | 0.128 | - |
| 1.7310 | 7000 | 0.1233 | - |
| 1.8546 | 7500 | 0.1221 | - |
| 1.9782 | 8000 | 0.1232 | - |
| 2.1019 | 8500 | 0.0841 | - |
| 2.2255 | 9000 | 0.0757 | - |
| 2.3492 | 9500 | 0.0764 | - |
| 2.4728 | 10000 | 0.0761 | - |
| 2.5964 | 10500 | 0.0726 | - |
| 2.7201 | 11000 | 0.0644 | - |
| 2.8437 | 11500 | 0.073 | - |
| 2.9674 | 12000 | 0.0725 | - |
| -1 | -1 | - | 0.9162 |
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## 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",
}
```
#### TripletLoss
```bibtex
@misc{hermans2017defense,
title={In Defense of the Triplet Loss for Person Re-Identification},
author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
year={2017},
eprint={1703.07737},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
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