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 ---
library_name: sentence-transformers
metrics:
- negative_mse
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:25095
- loss:MSELoss
widget:
- source_sentence: mariknak pay ketdi a naabrasaak iti kulonganda
sentences:
- Nakuha nako ang usa ka kuptanan sa istorya ug nagsugod kini sa pagbati ug porma
nga akong gusto
- 'Ang kasarangang pag-ulan sa London, nga adunay kataas nga 10°C ug ang ubos nga
6°C. #LondonWeather #RainyDay'
- Controversial religious text causes uproar among community members
- source_sentence: "JUAN COLE: Ang Pagduso sa Islamic State sa Baghdad 'Usa ka\
\ Pagsulay Aron Mabawi ang Gikuha sa Bush Administration' \n"
sentences:
- Ang Touchdown nga Selebrasyon ni Antonio Brown Sexy Gihapon Alang sa NFL Bisan
ang duha ka pagduso makapasilo kanimo.
- Natuklasan ng mga siyentipiko ang mga bagong species ng nilalang sa malalim na
dagat
- i feel so glad doing this
- source_sentence: New Curriculum Standards to Be Implemented in All Schools Next
Year
sentences:
- "Climate Change This Week: Mega Methane, Tidal Power, and More \n"
- '@lilomatic Only in Zimbabwe where u find Opposition party for another Opposition
party.'
- "Ang mamumuno nga si Mike namulong sa Ferguson: 'Ang Hustisya Dili Kanunay\
\ Gisilbi' \n"
- source_sentence: i am so blessed and feel blessed to be able to share my creations
with you
sentences:
- "Ania ang Buhaton Sa World Cup Host Cities Gawas sa Pagtan-aw sa Soccer \n"
- "Hillary Clinton's 'Super Volunteers' Are Back And Ready For 2016 \n"
- Awan pay ti koriente para kadagiti paset ti Joburg kalpasan ti uram ti kable iti
uneg ti daga https://t.co/szuZa380Lr
- source_sentence: "3 Napateg nga Addang (iti Aniaman nga Edad) tapno Agsagana iti\
\ Matay \n"
sentences:
- EPIC! RAND PAUL Laughs at CNN’s Climate Hysteria…Schools Jake Tapper on Climate
Truth [Video]
- im feeling horrible
- 'Image: WC Provincial Disaster Management Centre https://t.co/EcNgpBhjcV'
model-index:
- name: SentenceTransformer
results:
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: Unknown
type: unknown
metrics:
- type: negative_mse
value: -0.2521140966564417
name: Negative Mse
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained. 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:** 128 tokens
- **Output Dimensionality:** 768 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: XLMRobertaModel
(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("sentence_transformers_model_id")
# Run inference
sentences = [
'3 Napateg nga Addang (iti Aniaman nga Edad) tapno Agsagana iti Matay \n',
'EPIC! RAND PAUL Laughs at CNN’s Climate Hysteria…Schools Jake Tapper on Climate Truth [Video]',
'Image: WC Provincial Disaster Management Centre https://t.co/EcNgpBhjcV',
]
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
#### Knowledge Distillation
* Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:------------|
| **negative_mse** | **-0.2521** |
<!--
## 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: 25,095 training samples
* Columns: <code>sentence_0</code> and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | label |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------|
| type | string | list |
| details | <ul><li>min: 4 tokens</li><li>mean: 23.49 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
* Samples:
| sentence_0 | label |
|:------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------|
| <code>A suicide bomber targeting a crowded market resulting in numerous fatalities</code> | <code>[-0.05337272211909294, -0.296869158744812, -0.005234384443610907, -0.017071111127734184, 0.01954558491706848, ...]</code> |
| <code>Jeb Bush To Meet With Charleston Pastors <br></code> | <code>[-0.025684779509902, 0.2293000966310501, -0.005389949772506952, 0.09448838979005814, 0.017471183091402054, ...]</code> |
| <code>New scientific research suggests link between air pollution and lung disease</code> | <code>[-0.12967786192893982, 0.19541345536708832, -0.0044404976069927216, -0.06291326135396957, -0.03776596114039421, ...]</code> |
* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 20
- `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`: 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`: 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`: 20
- `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`: 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 | negative_mse |
|:-------:|:----:|:-------------:|:------------:|
| 0.5089 | 200 | - | -0.3720 |
| 1.0 | 393 | - | -0.3428 |
| 1.0178 | 400 | - | -0.3437 |
| 1.2723 | 500 | 0.0024 | - |
| 1.5267 | 600 | - | -0.3262 |
| 2.0 | 786 | - | -0.3153 |
| 2.0356 | 800 | - | -0.3156 |
| 2.5445 | 1000 | 0.0018 | -0.3070 |
| 3.0 | 1179 | - | -0.3004 |
| 3.0534 | 1200 | - | -0.3005 |
| 3.5623 | 1400 | - | -0.2959 |
| 3.8168 | 1500 | 0.0015 | - |
| 4.0 | 1572 | - | -0.2907 |
| 4.0712 | 1600 | - | -0.2924 |
| 4.5802 | 1800 | - | -0.2863 |
| 5.0 | 1965 | - | -0.2831 |
| 5.0891 | 2000 | 0.0013 | -0.2841 |
| 5.5980 | 2200 | - | -0.2792 |
| 6.0 | 2358 | - | -0.2765 |
| 6.1069 | 2400 | - | -0.2774 |
| 6.3613 | 2500 | 0.0012 | - |
| 6.6158 | 2600 | - | -0.2734 |
| 7.0 | 2751 | - | -0.2716 |
| 7.1247 | 2800 | - | -0.2722 |
| 7.6336 | 3000 | 0.0011 | -0.2700 |
| 8.0 | 3144 | - | -0.2684 |
| 8.1425 | 3200 | - | -0.2683 |
| 8.6514 | 3400 | - | -0.2665 |
| 8.9059 | 3500 | 0.001 | - |
| 9.0 | 3537 | - | -0.2645 |
| 9.1603 | 3600 | - | -0.2649 |
| 9.6692 | 3800 | - | -0.2639 |
| 10.0 | 3930 | - | -0.2625 |
| 10.1781 | 4000 | 0.0009 | -0.2619 |
| 10.6870 | 4200 | - | -0.2615 |
| 11.0 | 4323 | - | -0.2594 |
| 11.1959 | 4400 | - | -0.2598 |
| 11.4504 | 4500 | 0.0009 | - |
| 11.7048 | 4600 | - | -0.2587 |
| 12.0 | 4716 | - | -0.2582 |
| 12.2137 | 4800 | - | -0.2586 |
| 12.7226 | 5000 | 0.0008 | -0.2573 |
| 13.0 | 5109 | - | -0.2568 |
| 13.2316 | 5200 | - | -0.2567 |
| 13.7405 | 5400 | - | -0.2564 |
| 13.9949 | 5500 | 0.0008 | - |
| 14.0 | 5502 | - | -0.2558 |
| 14.2494 | 5600 | - | -0.2560 |
| 14.7583 | 5800 | - | -0.2551 |
| 15.0 | 5895 | - | -0.2548 |
| 15.2672 | 6000 | 0.0008 | -0.2552 |
| 15.7761 | 6200 | - | -0.2540 |
| 16.0 | 6288 | - | -0.2534 |
| 16.2850 | 6400 | - | -0.2538 |
| 16.5394 | 6500 | 0.0008 | - |
| 16.7939 | 6600 | - | -0.2529 |
| 17.0 | 6681 | - | -0.2532 |
| 17.3028 | 6800 | - | -0.2530 |
| 17.8117 | 7000 | 0.0008 | -0.2528 |
| 18.0 | 7074 | - | -0.2525 |
| 18.3206 | 7200 | - | -0.2527 |
| 18.8295 | 7400 | - | -0.2521 |
### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.1.1
- Transformers: 4.44.2
- PyTorch: 2.4.0
- Accelerate: 0.34.2
- Datasets: 3.0.0
- 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",
}
```
#### MSELoss
```bibtex
@inproceedings{reimers-2020-multilingual-sentence-bert,
title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2020",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2004.09813",
}
```
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