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---
tags:
- sentence-transformers
- cross-encoder
- reranker
- generated_from_trainer
- dataset_size:1548
- loss:BinaryCrossEntropyLoss
base_model: Alibaba-NLP/gte-multilingual-reranker-base
pipeline_tag: text-ranking
library_name: sentence-transformers
---
# CrossEncoder based on Alibaba-NLP/gte-multilingual-reranker-base
This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [Alibaba-NLP/gte-multilingual-reranker-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-reranker-base) on the json dataset using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
## Model Details
### Model Description
- **Model Type:** Cross Encoder
- **Base model:** [Alibaba-NLP/gte-multilingual-reranker-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-reranker-base) <!-- at revision 8215cf04918ba6f7b6a62bb44238ce2953d8831c -->
- **Maximum Sequence Length:** 8192 tokens
- **Number of Output Labels:** 1 label
- **Training Dataset:**
- json
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
## 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 CrossEncoder
# Download from the 🤗 Hub
model = CrossEncoder("cross_encoder_model_id")
# Get scores for pairs of texts
pairs = [
['приймати позу самовдоволеного, виявляючи пиху, зазнайство', 'having a mutual understanding or shared thoughts'],
['приймати позу самовдоволеного, виявляючи пиху, зазнайство', 'in trouble or state of shame'],
['приймати позу самовдоволеного, виявляючи пиху, зазнайство', 'someone who scrounges from others'],
['приймати позу самовдоволеного, виявляючи пиху, зазнайство', 'to subtly and indirectly seek praise, validation, or admiration from others'],
['приймати позу самовдоволеного, виявляючи пиху, зазнайство', 'to be on the verge of doing something'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)
# Or rank different texts based on similarity to a single text
ranks = model.rank(
'приймати позу самовдоволеного, виявляючи пиху, зазнайство',
[
'having a mutual understanding or shared thoughts',
'in trouble or state of shame',
'someone who scrounges from others',
'to subtly and indirectly seek praise, validation, or admiration from others',
'to be on the verge of doing something',
]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</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: 1,548 training samples
* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | text1 | text2 | label |
|:--------|:-----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 5 characters</li><li>mean: 43.72 characters</li><li>max: 111 characters</li></ul> | <ul><li>min: 4 characters</li><li>mean: 44.34 characters</li><li>max: 139 characters</li></ul> | <ul><li>0: ~70.00%</li><li>1: ~30.00%</li></ul> |
* Samples:
| text1 | text2 | label |
|:--------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:---------------|
| <code>уживається для вираження повного заперечення; ні</code> | <code>уживається для повного заперечення змісту зазначеного слова; зовсім не (треба)</code> | <code>1</code> |
| <code>уживається для вираження повного заперечення; ні</code> | <code>уживається для вираження заперечення чогось</code> | <code>1</code> |
| <code>уживається для вираження повного заперечення; ні</code> | <code>уживається для повного заперечення змісту зазначеного слова; зовсім не (розбиратися)</code> | <code>1</code> |
* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
```json
{
"activation_fn": "torch.nn.modules.linear.Identity",
"pos_weight": null
}
```
### Evaluation Dataset
#### json
* Dataset: json
* Size: 225 evaluation samples
* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
* Approximate statistics based on the first 225 samples:
| | text1 | text2 | label |
|:--------|:------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 10 characters</li><li>mean: 48.52 characters</li><li>max: 156 characters</li></ul> | <ul><li>min: 6 characters</li><li>mean: 45.7 characters</li><li>max: 129 characters</li></ul> | <ul><li>0: ~81.78%</li><li>1: ~18.22%</li></ul> |
* Samples:
| text1 | text2 | label |
|:-----------------------------------------------------------------------|:--------------------------------------------------------------|:---------------|
| <code>приймати позу самовдоволеного, виявляючи пиху, зазнайство</code> | <code>having a mutual understanding or shared thoughts</code> | <code>0</code> |
| <code>приймати позу самовдоволеного, виявляючи пиху, зазнайство</code> | <code>in trouble or state of shame</code> | <code>0</code> |
| <code>приймати позу самовдоволеного, виявляючи пиху, зазнайство</code> | <code>someone who scrounges from others</code> | <code>0</code> |
* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
```json
{
"activation_fn": "torch.nn.modules.linear.Identity",
"pos_weight": null
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 2
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
#### 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`: 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
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-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`: 2
- `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}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `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
- `hub_revision`: None
- `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
- `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
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.5155 | 50 | 0.4717 |
| 1.0309 | 100 | 0.3624 |
| 1.5464 | 150 | 0.2148 |
### Framework Versions
- Python: 3.13.1
- Sentence Transformers: 5.1.0
- Transformers: 4.56.1
- PyTorch: 2.8.0+cpu
- Accelerate: 1.12.0
- Datasets: 4.4.1
- Tokenizers: 0.22.0
## 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",
}
```
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