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chart-reranker

Text Ranking
sentence-transformers
Safetensors
new
cross-encoder
reranker
Generated from Trainer
dataset_size:24588
loss:BinaryCrossEntropyLoss
custom_code
Eval Results (legacy)
text-embeddings-inference
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chart-reranker / README.md
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 ---
tags:
- sentence-transformers
- cross-encoder
- reranker
- generated_from_trainer
- dataset_size:24588
- loss:BinaryCrossEntropyLoss
base_model: Alibaba-NLP/gte-multilingual-reranker-base
pipeline_tag: text-ranking
library_name: sentence-transformers
metrics:
- pearson
- spearman
model-index:
- name: CrossEncoder based on Alibaba-NLP/gte-multilingual-reranker-base
results:
- task:
type: cross-encoder-correlation
name: Cross Encoder Correlation
dataset:
name: validation
type: validation
metrics:
- type: pearson
value: 0.875500492479389
name: Pearson
- type: spearman
value: 0.8709281334702662
name: Spearman
---
# 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) 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:** 512 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/huggingface/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 = [
['What is the average rent price in Canada?', 'Title: "How many hours do Americans sleep at night (United States)"\nCollections: YouGov Trackers\nDatasets: YouGovTrackerValueV2\nChart Type: survey:timeseries\nSources: YouGov'],
['for the topic digital foortprint and identity use "\t " to give a description on if there was an provided teaching materials for this activity.', 'Title: "Different ways Americans define gender for someone who says they are transgender (United States)"\nCollections: YouGov Trackers\nDatasets: YouGovTrackerValueV2\nChart Type: survey:timeseries\nSources: YouGov'],
['Which U.S. cities or counties have the highest rates of aggravated assault involving a deadly weapon per 100,000 residents?', 'Title: "U.S. Bank Overview, CITY Overview"\nCollections: Companies\nDatasets: InstrumentClosePrice1Day\nChart Type: timeseries:eav_v3\nCanonical forms: "U.S. Bancorp"="closing_price", "Club De Futbol Intercity Sad"="closing_price"'],
['Black identity topics', 'Title: "Different ways Americans define gender for someone who says they are transgender (United States)"\nCollections: YouGov Trackers\nDatasets: YouGovTrackerValueV2\nChart Type: survey:timeseries\nSources: YouGov'],
['Which company in the Interactive Media and Services category has the highest market capitalization?', 'Title: "DigiPlus Interactive. Capital Expenditure (Quarterly)"\nCollections: Companies\nDatasets: StandardIncomeStatement\nChart Type: timeseries:eav_v3\nCanonical forms: "Capital Expenditure"="capital_expenditure"\nSources: S&P Global'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)
# Or rank different texts based on similarity to a single text
ranks = model.rank(
'What is the average rent price in Canada?',
[
'Title: "How many hours do Americans sleep at night (United States)"\nCollections: YouGov Trackers\nDatasets: YouGovTrackerValueV2\nChart Type: survey:timeseries\nSources: YouGov',
'Title: "Different ways Americans define gender for someone who says they are transgender (United States)"\nCollections: YouGov Trackers\nDatasets: YouGovTrackerValueV2\nChart Type: survey:timeseries\nSources: YouGov',
'Title: "U.S. Bank Overview, CITY Overview"\nCollections: Companies\nDatasets: InstrumentClosePrice1Day\nChart Type: timeseries:eav_v3\nCanonical forms: "U.S. Bancorp"="closing_price", "Club De Futbol Intercity Sad"="closing_price"',
'Title: "Different ways Americans define gender for someone who says they are transgender (United States)"\nCollections: YouGov Trackers\nDatasets: YouGovTrackerValueV2\nChart Type: survey:timeseries\nSources: YouGov',
'Title: "DigiPlus Interactive. Capital Expenditure (Quarterly)"\nCollections: Companies\nDatasets: StandardIncomeStatement\nChart Type: timeseries:eav_v3\nCanonical forms: "Capital Expenditure"="capital_expenditure"\nSources: S&P Global',
]
)
# [{'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
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## Evaluation
### Metrics
#### Cross Encoder Correlation
* Dataset: `validation`
* Evaluated with [<code>CrossEncoderCorrelationEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderCorrelationEvaluator)
| Metric | Value |
|:-------------|:-----------|
| pearson | 0.8755 |
| **spearman** | **0.8709** |
<!--
## 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: 24,588 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 3 characters</li><li>mean: 88.65 characters</li><li>max: 998 characters</li></ul> | <ul><li>min: 73 characters</li><li>mean: 169.97 characters</li><li>max: 352 characters</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.41</li><li>max: 1.0</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|
| <code>What is the average rent price in Canada?</code> | <code>Title: "How many hours do Americans sleep at night (United States)"<br>Collections: YouGov Trackers<br>Datasets: YouGovTrackerValueV2<br>Chart Type: survey:timeseries<br>Sources: YouGov</code> | <code>0.0</code> |
| <code>for the topic digital foortprint and identity use " " to give a description on if there was an provided teaching materials for this activity.</code> | <code>Title: "Different ways Americans define gender for someone who says they are transgender (United States)"<br>Collections: YouGov Trackers<br>Datasets: YouGovTrackerValueV2<br>Chart Type: survey:timeseries<br>Sources: YouGov</code> | <code>0.25</code> |
| <code>Which U.S. cities or counties have the highest rates of aggravated assault involving a deadly weapon per 100,000 residents?</code> | <code>Title: "U.S. Bank Overview, CITY Overview"<br>Collections: Companies<br>Datasets: InstrumentClosePrice1Day<br>Chart Type: timeseries:eav_v3<br>Canonical forms: "U.S. Bancorp"="closing_price", "Club De Futbol Intercity Sad"="closing_price"</code> | <code>0.0</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`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 5
- `fp16`: 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`: 32
- `per_device_eval_batch_size`: 32
- `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`: 5
- `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
- `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`: 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}
- `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
- `project`: huggingface
- `trackio_space_id`: trackio
- `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`: no
- `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`: True
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | validation_spearman |
|:------:|:----:|:-------------:|:-------------------:|
| 0.1300 | 100 | - | 0.7581 |
| 0.2601 | 200 | - | 0.7928 |
| 0.3901 | 300 | - | 0.8105 |
| 0.5202 | 400 | - | 0.8252 |
| 0.6502 | 500 | 0.4726 | 0.8306 |
| 0.7802 | 600 | - | 0.8338 |
| 0.9103 | 700 | - | 0.8398 |
| 1.0 | 769 | - | 0.8406 |
| 1.0403 | 800 | - | 0.8412 |
| 1.1704 | 900 | - | 0.8479 |
| 1.3004 | 1000 | 0.4027 | 0.8525 |
| 1.4304 | 1100 | - | 0.8521 |
| 1.5605 | 1200 | - | 0.8549 |
| 1.6905 | 1300 | - | 0.8591 |
| 1.8205 | 1400 | - | 0.8619 |
| 1.9506 | 1500 | 0.3793 | 0.8614 |
| 2.0 | 1538 | - | 0.8627 |
| 2.0806 | 1600 | - | 0.8623 |
| 2.2107 | 1700 | - | 0.8641 |
| 2.3407 | 1800 | - | 0.8598 |
| 2.4707 | 1900 | - | 0.8655 |
| 2.6008 | 2000 | 0.3534 | 0.8641 |
| 2.7308 | 2100 | - | 0.8651 |
| 2.8609 | 2200 | - | 0.8656 |
| 2.9909 | 2300 | - | 0.8668 |
| 3.0 | 2307 | - | 0.8660 |
| 3.1209 | 2400 | - | 0.8678 |
| 3.2510 | 2500 | 0.3387 | 0.8654 |
| 3.3810 | 2600 | - | 0.8654 |
| 3.5111 | 2700 | - | 0.8667 |
| 3.6411 | 2800 | - | 0.8676 |
| 3.7711 | 2900 | - | 0.8674 |
| 3.9012 | 3000 | 0.3335 | 0.8704 |
| 4.0 | 3076 | - | 0.8703 |
| 4.0312 | 3100 | - | 0.8698 |
| 4.1612 | 3200 | - | 0.8709 |
### Framework Versions
- Python: 3.12.11
- Sentence Transformers: 5.1.2
- Transformers: 4.57.1
- PyTorch: 2.8.0+cu128
- Accelerate: 1.11.0
- Datasets: 4.2.0
- Tokenizers: 0.22.1
## 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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