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---
base_model: djovak/embedic-large
library_name: sentence-transformers
metrics:
- cosine_accuracy
- dot_accuracy
- manhattan_accuracy
- euclidean_accuracy
- max_accuracy
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:176
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Kako Meridian kladionica tretira odlaganja?
sentences:
- Klađenje na dubl se razlikuje od singla, gde timska hemija i tehnika igrača poput
voleja i servisa igraju ključnu ulogu.
- Važno je pratiti formu igrača, promene u servisu i povrede kako biste doneli informisane
odluke u klađenju uživo na tenis.
- Meridian kladionica računa kvotu 1.00 ako se utakmica ne odigra u roku od sledećeg
dana od planiranog termina.
- source_sentence: Šta je hajnc sistem klađenja?
sentences:
- Loši vremenski uslovi, kao što su kiša ili sneg, mogu otežati igru i smanjiti
broj golova.
- Hajnc je kombinovani sistem klađenja koji uključuje šest događaja i ukupno 57
pojedinačnih opklada.
- Za razliku od drugih sportova, vremenski uslovi ne utiču direktno na košarkaške
mečeve, ali povrede ili izostanci igrača mogu značajno promeniti ishod.
- source_sentence: Kako funkcioniše klađenje na poluvreme/kraj?
sentences:
- Ako se utakmica odloži za više od 48 sati, kvota postaje 1.00 i ulog se vraća,
osim ako su ostali parovi na tiketu.
- Ova vrsta klađenja zahteva predviđanje ishoda i na poluvremenu i na kraju utakmice.
Na primer, opklada 1-2 znači da domaćin vodi na poluvremenu, ali gost pobeđuje
na kraju.
- Sistem 'Srećni 31' uključuje pet događaja i ukupno 31 pojedinačnu opkladu koja
obuhvata singl, dubl, trostruke, četvorostruke i petostruke opklade.
- source_sentence: Kako koristiti informacije za klađenje?
sentences:
- Informacije su ključne za uspešno klađenje. Preporučuje se korišćenje službenih
sportskih stranica, portala i aplikacija za vesti kako biste dobili najnovije
podatke o utakmicama i igračima.
- Koeficijenti, ili kvote, označavaju verovatnoću ishoda događaja i određuju potencijalni
dobitak na osnovu uloženog novca.
- Hendikep klađenje podrazumeva da slabiji tim dobija prednost u bodovima pre početka
meča, čime se izjednačavaju šanse za pobedu.
- source_sentence: Šta je klađenje na kartone?
sentences:
- Ravnomerno klađenje podrazumeva postavljanje istog uloga na svaki događaj kako
bi se smanjio rizik.
- Klađenje na broj kornera podrazumeva predviđanje koliko će kornera biti izvedeno
tokom meča, sa kvotama koje se menjaju uživo.
- Klađenje na kartone uključuje predviđanje broja žutih ili crvenih kartona na utakmici,
postavljajući granicu pre početka utakmice.
model-index:
- name: SentenceTransformer based on djovak/embedic-large
results:
- task:
type: triplet
name: Triplet
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy
value: 1.0
name: Cosine Accuracy
- type: dot_accuracy
value: 0.0
name: Dot Accuracy
- type: manhattan_accuracy
value: 1.0
name: Manhattan Accuracy
- type: euclidean_accuracy
value: 1.0
name: Euclidean Accuracy
- type: max_accuracy
value: 1.0
name: Max Accuracy
---
# SentenceTransformer based on djovak/embedic-large
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [djovak/embedic-large](https://huggingface.co/djovak/embedic-large). It maps sentences & paragraphs to a 1024-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:** [djovak/embedic-large](https://huggingface.co/djovak/embedic-large) <!-- at revision 4d275ee32c11e1e2a1de8dc59493551c8e2bc4c8 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 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': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, '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 = [
'Šta je klađenje na kartone?',
'Klađenje na kartone uključuje predviđanje broja žutih ili crvenih kartona na utakmici, postavljajući granicu pre početka utakmice.',
'Ravnomerno klađenje podrazumeva postavljanje istog uloga na svaki događaj kako bi se smanjio rizik.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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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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## 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 | 1.0 |
| dot_accuracy | 0.0 |
| manhattan_accuracy | 1.0 |
| euclidean_accuracy | 1.0 |
| **max_accuracy** | **1.0** |
<!--
## Bias, Risks and Limitations
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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: 176 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 176 samples:
| | anchor | positive | negative |
|:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 12.9 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 33.32 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 33.6 tokens</li><li>max: 54 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:--------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Kako funkcioniše klađenje uživo na tenis?</code> | <code>Klađenje uživo na tenis omogućava predviđanje ishoda poena, gemova i setova tokom meča, sa stalnim promenama kvota u zavisnosti od igre.</code> | <code>Trixie sistem klađenja uključuje četiri opklade na tri različita događaja: tri dubl opklade i jednu trostruku opkladu. Dovoljna su dva pogođena događaja da bi se ostvario dobitak.</code> |
| <code>Šta je klađenje na produžetke?</code> | <code>Klađenje na produžetke uključuje opklade na ishod utakmice u dodatnim periodima igre, nakon regularnog vremena.</code> | <code>Najpopularnije lige za klađenje na hokej uključuju NHL, Kontinentalnu ligu (KHL) i švedsku hokejašku ligu.</code> |
| <code>Kako pandemija COVID-19 utiče na otkazivanje utakmica?</code> | <code>Pandemija COVID-19 je dovela do povećanja broja otkazanih utakmica zbog zdravstvenih protokola i izolacija igrača.</code> | <code>Hendikep dodaje golove slabijem timu kako bi se izjednačile šanse za oba tima.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 45 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 45 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 12.89 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 35.67 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 32.58 tokens</li><li>max: 54 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Kako DNB funkcioniše u klađenju uživo?</code> | <code>U klađenju uživo, DNB postaje profitabilan kada autsajder postigne gol prvi, povećavajući kvote za favorita.</code> | <code>Klađenje na kornere omogućava predviđanje broja kornera na utakmici. Igrač se kladi da li će broj kornera biti iznad ili ispod postavljene granice.</code> |
| <code>Šta se dešava sa opkladama u slučaju promenjenog mesta događaja?</code> | <code>Ako se promeni mesto održavanja utakmice, opklade postaju nevažeće, a kvota se računa kao 1.00.</code> | <code>Ako se utakmica pomeri za više od 48 sati, kladionica proglašava događaj nevažećim i kvota postaje 1.00.</code> |
| <code>Šta je azijski hendikep?</code> | <code>Azijski hendikep daje jednom timu prednost pre početka utakmice, a opklada se deli na dve odvojene opklade kako bi se izjednačile šanse za pobedu.</code> | <code>Najčešći tipovi klađenja uključuju konačan ishod, hendikep, ukupan broj poena, i klađenje na performanse pojedinih igrača.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_eval_batch_size`: 16
- `learning_rate`: 3e-05
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `ddp_find_unused_parameters`: False
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `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`: 3e-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
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `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`: True
- `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`: False
- `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
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | max_accuracy |
|:------:|:----:|:-------------:|:------:|:------------:|
| 0.1818 | 4 | 0.5902 | - | - |
| 0.3636 | 8 | 0.2228 | - | - |
| 0.5455 | 12 | 0.1065 | - | - |
| 0.7273 | 16 | 0.0107 | - | - |
| 0.9091 | 20 | 0.0801 | - | - |
| 1.0 | 22 | - | 0.1504 | 1.0 |
### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.1.1
- Transformers: 4.45.0
- PyTorch: 2.3.0
- Accelerate: 0.34.1
- Datasets: 2.19.1
- Tokenizers: 0.20.0
## 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",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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