File size: 17,445 Bytes
b90c7e1 d0f5469 b90c7e1 f12de44 b90c7e1 f12de44 b90c7e1 d0f5469 b90c7e1 d0f5469 b90c7e1 f12de44 b90c7e1 f12de44 b90c7e1 f12de44 b90c7e1 f12de44 b90c7e1 d0f5469 b90c7e1 d0f5469 b90c7e1 d0f5469 b90c7e1 358ba33 d0f5469 b90c7e1 d0f5469 b90c7e1 40a79c5 d0f5469 b90c7e1 d0f5469 b90c7e1 1bb56d2 f12de44 b90c7e1 1bb56d2 b90c7e1 1bb56d2 b90c7e1 f12de44 b90c7e1 358ba33 d0f5469 b90c7e1 f12de44 b90c7e1 f12de44 b90c7e1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 |
---
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>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## 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.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## 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",
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |