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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:478146
- loss:CoSENTLoss
widget:
- source_sentence: However, its underutilization is mainly due to the absence of a
concrete and coherent dissemination strategy.
sentences:
- At the same time, they need to understand that living in Europe brings great responsibilities
in addition to great benefits.
- 'The mainstay of any intelligent and patriotic mineral policy can be summed up
in the following postulate: "since minerals are exhaustible, they should only
be exploited with the maximum return for the economy of the country where they
are mined".'
- We must move quickly to a shared sustainable energy supply, sustainable transportation
and clean air.
- source_sentence: Their track record shows they do not support Australia<92>s traditional
industries because they are constantly pandering to the Greens.
sentences:
- An economic dynamic based on the sustainable development of national potential,
equitable access to the means of production, social justice, environmental conservation,
the incorporation of added value, the promotion of competitiveness and self-management,
- the cry "El campo no aguanta más" (The countryside can't take it anymore), of
the peasant movement and its proclamation of "Salvemos al Campo para salvar a
México" (Let's save the countryside to save Mexico);
- On the other hand, increasing defence capacity is directly related to the involvement
of all citizens in appropriate programmes, which, together with the acquisition
of skills, experience and organisation, also contribute to forging a spirit of
militancy and collectivity.
- source_sentence: We will prepare the proposals of the United Nations Declaration
on the Rights of the Child in line with the commitments made.
sentences:
- For the presentation of Czech culture, we will also use the upcoming major anniversaries
(100 years of the founding of Czechoslovakia, the 30th anniversary of the canonization
of Agnes of Bohemia, 600 years since the birth of George of Poděbrady, etc.).
- Separate prison units for young people should be established, and special rehabilitation
measures should be introduced in these units.
- Austrian citizenship is a valuable asset and should not become accessible to those
who do not abide by the laws of our state.
- source_sentence: Third, CD&V wants to strengthen the social sustainability of our
agriculture and horticulture sector.
sentences:
- We will take a farm-level approach where possible so that low-emissions farmers
are rewarded with a lower cost through the ETS, rather than the current approach
that assumes each cow, for instance, has the same emissions on every farm.
- In addition, 20 billion euros in tax revenues are fraudulently evaded every year
(the equivalent of the healthcare budget).
- 87 percent of arrested undocumented migrants are released sooner or later, but
without papers, in a lawless situation.
- source_sentence: This incites social hatred, threatens economic and social stability,
and undermines trust in the authorities.
sentences:
- ' The conditions for a healthy entrepreneurship, where the most innovative and
creative win and where the source of enrichment cannot be property speculation
or guilds and networks. '
- According to statistics from the Attorney General's Office, since February 2005,
when the implementation of the PSD was announced, the rate of violent deaths per
100,000 inhabitants has dropped from 26.41 in December 2005 to 18.43 in December
2007.
- As a result, the profits of the oligarchs are more than 400 times what our entire
country gets from the exploitation of natural resources.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained on the json dataset. 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:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- json
<!-- - **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': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel
(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})
)
```
## 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("LequeuISIR/final-DPR-8e-05")
# Run inference
sentences = [
'This incites social hatred, threatens economic and social stability, and undermines trust in the authorities.',
'\xa0The conditions for a healthy entrepreneurship, where the most innovative and creative win and where the source of enrichment cannot be property speculation or guilds and networks. ',
'As a result, the profits of the oligarchs are more than 400 times what our entire country gets from the exploitation of natural resources.',
]
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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</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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## Training Details
### Training Dataset
#### json
* Dataset: json
* Size: 478,146 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 17 tokens</li><li>mean: 33.73 tokens</li><li>max: 107 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 33.84 tokens</li><li>max: 101 tokens</li></ul> | <ul><li>0: ~57.50%</li><li>1: ~4.10%</li><li>2: ~38.40%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>There have also been other important structural changes in the countryside, which have come together to form this new, as yet unknown, country.</code> | <code>Meanwhile, investment, which is the way to increase production, employment capacity and competitiveness of the economy, fell from 20% of output in 1974 to only 11.8% on average between 1984 and 1988.</code> | <code>0</code> |
| <code>Introduce new visa categories so we can be responsive to humanitarian needs and incentivise greater investment in our domestic infrastructure and regional economies</code> | <code>The purpose of the project is to design and implement public policies aimed at achieving greater and faster inclusion of immigrants.</code> | <code>2</code> |
| <code>and economic crimes that seriously and generally affect the fundamental rights of individuals and the international community as a whole.</code> | <code>For the first time in the history, not only of Ecuador, but of the entire world, a government promoted a public audit process of the foreign debt and declared some of its tranches illegitimate and immoral.</code> | <code>0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Evaluation Dataset
#### json
* Dataset: json
* Size: 478,146 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 17 tokens</li><li>mean: 33.62 tokens</li><li>max: 103 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 34.48 tokens</li><li>max: 111 tokens</li></ul> | <ul><li>0: ~57.30%</li><li>1: ~2.90%</li><li>2: ~39.80%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>The anchoring of the Slovak Republic in the European Union allows citizens to feel: secure politically, secure economically, secure socially.</code> | <code>Radikale Venstre wants Denmark to participate fully and firmly in EU cooperation on immigration, asylum and cross-border crime.</code> | <code>2</code> |
| <code>Portugal's participation in the Community's negotiation of the next financial perspective should also be geared in the same direction.</code> | <code>Given the dynamic international framework, safeguarding the national interest requires adjustments to each of these vectors.</code> | <code>2</code> |
| <code>On asylum, the Green Party will: Dismantle the direct provision system and replace it with an efficient and humane system for determining the status of asylum seekers</code> | <code>The crisis in the coal sector subsequently forced these immigrant workers to move into other economic sectors such as metallurgy, chemicals, construction and transport.</code> | <code>2</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 8e-05
- `num_train_epochs`: 5
- `warmup_ratio`: 0.05
- `bf16`: True
- `batch_sampler`: no_duplicates
#### 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`: 64
- `per_device_eval_batch_size`: 64
- `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`: 8e-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`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.05
- `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`: True
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0837 | 500 | 0.7889 | 9.5828 |
| 0.1673 | 1000 | 1.2158 | 9.3274 |
| 0.2510 | 1500 | 1.8215 | 9.4274 |
| 0.3346 | 2000 | 2.3548 | 8.2583 |
| 0.4183 | 2500 | 2.7493 | 8.1446 |
| 0.5019 | 3000 | 2.8998 | 7.9046 |
| 0.5856 | 3500 | 2.9298 | 8.0640 |
| 0.6692 | 4000 | 2.9053 | 7.2746 |
| 0.7529 | 4500 | 3.0905 | 7.5099 |
| 0.8365 | 5000 | 3.1864 | 7.3883 |
| 0.9202 | 5500 | 3.2322 | 6.9968 |
| 1.0038 | 6000 | 3.1194 | 7.4682 |
| 1.0875 | 6500 | 3.0122 | 7.7295 |
| 1.1712 | 7000 | 3.0453 | 7.1696 |
| 1.2548 | 7500 | 2.9439 | 7.2775 |
| 1.3385 | 8000 | 3.1108 | 7.4838 |
| 1.4221 | 8500 | 2.8512 | 7.5204 |
| 1.5058 | 9000 | 2.9865 | 7.4528 |
| 1.5894 | 9500 | 2.9995 | 8.0682 |
| 1.6731 | 10000 | 3.1073 | 7.5344 |
| 1.7567 | 10500 | 3.0631 | 7.4572 |
| 1.8404 | 11000 | 2.9915 | 7.4961 |
| 1.9240 | 11500 | 3.0445 | 7.3575 |
| 2.0077 | 12000 | 2.9501 | 7.9786 |
| 2.0914 | 12500 | 2.3377 | 8.6208 |
| 2.1750 | 13000 | 2.2833 | 8.8356 |
| 2.2587 | 13500 | 2.2785 | 8.8709 |
| 2.3423 | 14000 | 2.3012 | 8.6250 |
| 2.4260 | 14500 | 2.3488 | 8.1099 |
| 2.5096 | 15000 | 2.095 | 9.2305 |
| 2.5933 | 15500 | 2.4123 | 8.6405 |
| 2.6769 | 16000 | 2.2236 | 8.7805 |
| 2.7606 | 16500 | 2.3367 | 8.7110 |
| 2.8442 | 17000 | 2.1159 | 8.6447 |
| 2.9279 | 17500 | 2.1622 | 8.7123 |
| 3.0115 | 18000 | 2.1916 | 9.0314 |
| 3.0952 | 18500 | 1.604 | 9.3373 |
| 3.1789 | 19000 | 1.4116 | 9.6509 |
| 3.2625 | 19500 | 1.4036 | 9.9127 |
| 3.3462 | 20000 | 1.5392 | 9.8093 |
| 3.4298 | 20500 | 1.5791 | 9.8325 |
| 3.5135 | 21000 | 1.5343 | 9.7822 |
| 3.5971 | 21500 | 1.3913 | 9.6243 |
| 3.6808 | 22000 | 1.5151 | 9.9644 |
| 3.7644 | 22500 | 1.3922 | 9.7816 |
| 3.8481 | 23000 | 1.3361 | 9.5338 |
| 3.9317 | 23500 | 1.3363 | 9.8282 |
| 4.0154 | 24000 | 1.2234 | 10.2117 |
| 4.0990 | 24500 | 0.5927 | 10.4107 |
| 4.1827 | 25000 | 0.6879 | 10.4405 |
| 4.2664 | 25500 | 0.6832 | 10.5138 |
| 4.3500 | 26000 | 0.6514 | 10.2798 |
| 4.4337 | 26500 | 0.7396 | 10.3250 |
| 4.5173 | 27000 | 0.6813 | 10.4115 |
| 4.6010 | 27500 | 0.765 | 10.1365 |
| 4.6846 | 28000 | 0.5915 | 10.2402 |
| 4.7683 | 28500 | 0.5028 | 10.3197 |
| 4.8519 | 29000 | 0.5306 | 10.3270 |
| 4.9356 | 29500 | 0.5886 | 10.3543 |
### Framework Versions
- Python: 3.9.21
- Sentence Transformers: 3.4.0
- Transformers: 4.48.1
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.2.0
- 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",
}
```
#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
author={Su Jianlin},
year={2022},
month={Jan},
url={https://kexue.fm/archives/8847},
}
```
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