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---
tags:
- sentence-transformers
- cross-encoder
- generated_from_trainer
- dataset_size:1314940
- loss:BinaryCrossEntropyLoss
base_model: cross-encoder/stsb-distilroberta-base
pipeline_tag: text-ranking
library_name: sentence-transformers
metrics:
- map
- mrr@50
- ndcg@50
model-index:
- name: CrossEncoder based on cross-encoder/stsb-distilroberta-base
results:
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: reranking dev
type: reranking-dev
metrics:
- type: map
value: 0.7207
name: Map
- type: mrr@50
value: 0.7903
name: Mrr@50
- type: ndcg@50
value: 0.8072
name: Ndcg@50
---
# CrossEncoder based on cross-encoder/stsb-distilroberta-base
This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [cross-encoder/stsb-distilroberta-base](https://huggingface.co/cross-encoder/stsb-distilroberta-base) 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:** [cross-encoder/stsb-distilroberta-base](https://huggingface.co/cross-encoder/stsb-distilroberta-base) <!-- at revision 6b71347df6e2b34246b53e06d6bce70ef67de368 -->
- **Maximum Sequence Length:** 128 tokens
- **Number of Output Labels:** 1 label
<!-- - **Training Dataset:** Unknown -->
<!-- - **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 = [
['Triangles and polygons. Space, shape and measurement. Form 1. Malawi Mathematics Syllabus. Learning outcomes: students must be able to solve problems involving angles, triangles and polygons including: types of triangles, calculate the interior and exterior angles of a triangle, different types of polygons, interior angles and', 'Discussing different types of quadrilaterals. Quadrilateral is a closed figure with four line segments. Each point where the two line segments meet is called a vertex. The closed figure also form four angles.. Discussing different types of quadrilaterals Albert Mhango, Mzimba Introduction: Quad'],
['Triangles and polygons. Space, shape and measurement. Form 1. Malawi Mathematics Syllabus. Learning outcomes: students must be able to solve problems involving angles, triangles and polygons including: types of triangles, calculate the interior and exterior angles of a triangle, different types of polygons, interior angles and', 'Discussing properties of quadrilaterals. The common properties that you will see in every quadrilateral include; all quadrilaterals have four sides, they all consist of four vertices and the sum of interior angles is equal to 360 degrees.. Discussing properties of quadrilaterals Albert Mhango,'],
['Triangles and polygons. Space, shape and measurement. Form 1. Malawi Mathematics Syllabus. Learning outcomes: students must be able to solve problems involving angles, triangles and polygons including: types of triangles, calculate the interior and exterior angles of a triangle, different types of polygons, interior angles and', 'Calculating interior and exterior angles of a triangle. The exterior angle of a triangle is equal to the sum of two opposite interior angles. This property will help you to find angles in a triangle and exterior angles.. Calculating interior and exterior angles of a triangle Albert Mhango, Mzimba Introduction: The'],
['Triangles and polygons. Space, shape and measurement. Form 1. Malawi Mathematics Syllabus. Learning outcomes: students must be able to solve problems involving angles, triangles and polygons including: types of triangles, calculate the interior and exterior angles of a triangle, different types of polygons, interior angles and', 'Using properties of quadrilaterals to solve problems. A quadrilateral is a geometric figure with four sides. The general properties of quadrilaterals include; they all have four sides, have two diagonals, have four interior angles and the sum of their interior angles is equal to 360 degrees.. Using properties'],
['Triangles and polygons. Space, shape and measurement. Form 1. Malawi Mathematics Syllabus. Learning outcomes: students must be able to solve problems involving angles, triangles and polygons including: types of triangles, calculate the interior and exterior angles of a triangle, different types of polygons, interior angles and', 'Solving Problems Involving Polygons. In this lesson, you will learn how we can use the exterior angles of polygon formula to solve problems.. Solving Problems Involving Polygons Mary Chagwa, Blantyre Introduction: In the previous lesson, you were deriving the formula for finding the sum'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)
# Or rank different texts based on similarity to a single text
ranks = model.rank(
'Triangles and polygons. Space, shape and measurement. Form 1. Malawi Mathematics Syllabus. Learning outcomes: students must be able to solve problems involving angles, triangles and polygons including: types of triangles, calculate the interior and exterior angles of a triangle, different types of polygons, interior angles and',
[
'Discussing different types of quadrilaterals. Quadrilateral is a closed figure with four line segments. Each point where the two line segments meet is called a vertex. The closed figure also form four angles.. Discussing different types of quadrilaterals Albert Mhango, Mzimba Introduction: Quad',
'Discussing properties of quadrilaterals. The common properties that you will see in every quadrilateral include; all quadrilaterals have four sides, they all consist of four vertices and the sum of interior angles is equal to 360 degrees.. Discussing properties of quadrilaterals Albert Mhango,',
'Calculating interior and exterior angles of a triangle. The exterior angle of a triangle is equal to the sum of two opposite interior angles. This property will help you to find angles in a triangle and exterior angles.. Calculating interior and exterior angles of a triangle Albert Mhango, Mzimba Introduction: The',
'Using properties of quadrilaterals to solve problems. A quadrilateral is a geometric figure with four sides. The general properties of quadrilaterals include; they all have four sides, have two diagonals, have four interior angles and the sum of their interior angles is equal to 360 degrees.. Using properties',
'Solving Problems Involving Polygons. In this lesson, you will learn how we can use the exterior angles of polygon formula to solve problems.. Solving Problems Involving Polygons Mary Chagwa, Blantyre Introduction: In the previous lesson, you were deriving the formula for finding the sum',
]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### 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
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Evaluation
### Metrics
#### Cross Encoder Reranking
* Dataset: `reranking-dev`
* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
```json
{
"at_k": 50,
"always_rerank_positives": false
}
```
| Metric | Value |
|:------------|:---------------------|
| map | 0.7207 (+0.0992) |
| mrr@50 | 0.7903 (+0.0528) |
| **ndcg@50** | **0.8072 (+0.0817)** |
<!--
## 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: 1,314,940 training samples
* Columns: <code>topic</code>, <code>content</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | topic | content | label |
|:--------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 42 characters</li><li>mean: 158.98 characters</li><li>max: 336 characters</li></ul> | <ul><li>min: 7 characters</li><li>mean: 150.81 characters</li><li>max: 353 characters</li></ul> | <ul><li>0: ~76.30%</li><li>1: ~23.70%</li></ul> |
* Samples:
| topic | content | label |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>Triangles and polygons. Space, shape and measurement. Form 1. Malawi Mathematics Syllabus. Learning outcomes: students must be able to solve problems involving angles, triangles and polygons including: types of triangles, calculate the interior and exterior angles of a triangle, different types of polygons, interior angles and</code> | <code>Discussing different types of quadrilaterals. Quadrilateral is a closed figure with four line segments. Each point where the two line segments meet is called a vertex. The closed figure also form four angles.. Discussing different types of quadrilaterals Albert Mhango, Mzimba Introduction: Quad</code> | <code>1</code> |
| <code>Triangles and polygons. Space, shape and measurement. Form 1. Malawi Mathematics Syllabus. Learning outcomes: students must be able to solve problems involving angles, triangles and polygons including: types of triangles, calculate the interior and exterior angles of a triangle, different types of polygons, interior angles and</code> | <code>Discussing properties of quadrilaterals. The common properties that you will see in every quadrilateral include; all quadrilaterals have four sides, they all consist of four vertices and the sum of interior angles is equal to 360 degrees.. Discussing properties of quadrilaterals Albert Mhango,</code> | <code>1</code> |
| <code>Triangles and polygons. Space, shape and measurement. Form 1. Malawi Mathematics Syllabus. Learning outcomes: students must be able to solve problems involving angles, triangles and polygons including: types of triangles, calculate the interior and exterior angles of a triangle, different types of polygons, interior angles and</code> | <code>Calculating interior and exterior angles of a triangle. The exterior angle of a triangle is equal to the sum of two opposite interior angles. This property will help you to find angles in a triangle and exterior angles.. Calculating interior and exterior angles of a triangle Albert Mhango, Mzimba Introduction: The</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": 3.7016043663024902
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `learning_rate`: 2e-05
- `warmup_ratio`: 0.1
- `seed`: 12
- `bf16`: True
- `dataloader_num_workers`: 4
- `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`: 128
- `per_device_eval_batch_size`: 128
- `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`: 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`: 12
- `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`: 4
- `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`: 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
- `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 | reranking-dev_ndcg@50 |
|:------:|:-----:|:-------------:|:---------------------:|
| 0.0001 | 1 | 1.0174 | - |
| 0.9999 | 10272 | 0.6234 | 0.7913 (+0.0658) |
| 1.9998 | 20544 | 0.3901 | 0.8041 (+0.0786) |
| 2.9997 | 30816 | 0.2978 | 0.8072 (+0.0817) |
### Framework Versions
- Python: 3.11.13
- Sentence Transformers: 4.1.0
- Transformers: 4.52.4
- PyTorch: 2.6.0+cu124
- Accelerate: 1.7.0
- Datasets: 2.14.4
- Tokenizers: 0.21.1
## 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",
}
```
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