Update tag to text ranking

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	---
	language:
	- en
	license: apache-2.0
	tags:
	- sentence-transformers
	- cross-encoder
	- text-classification
	- generated_from_trainer
	- dataset_size:578402
	- loss:BinaryCrossEntropyLoss
	pipeline_tag: text-ranking
	library_name: sentence-transformers
	metrics:
	- map
	- mrr@10
	- ndcg@10
	model-index:
	- name: NeoBERT-medium trained on GooAQ
	results: []
	---

	# NeoBERT-medium trained on GooAQ

	This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model trained using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

	## Model Details

	### Model Description
	- Model Type: Cross Encoder
	<!-- - Base model: [Unknown](https://huggingface.co/unknown) -->
	- Maximum Sequence Length: 4096 tokens
	- Number of Output Labels: 1 label
	<!-- - Training Dataset: Unknown -->
	- Language: en
	- License: apache-2.0

	### 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("tomaarsen/reranker-NeoBERT-gooaq-bce")
	# Get scores for pairs of texts
	pairs = [
	['what are the signs of a bad yeast infection?', "['Itching and irritation in the vagina and vulva.', 'A burning sensation, especially during intercourse or while urinating.', 'Redness and swelling of the vulva.', 'Vaginal pain and soreness.', 'Vaginal rash.', 'Thick, white, odor-free vaginal discharge with a cottage cheese appearance.', 'Watery vaginal discharge.']"],
	['what are the signs of a bad yeast infection?', 'Vaginal yeast infections can cause: itching and irritation in the vagina. redness, swelling, or itching of the vulva (the folds of skin outside the vagina) a thick, white discharge that can look like cottage cheese and is usually odorless, although it might smell like bread or yeast.'],
	['what are the signs of a bad yeast infection?', 'It can feel like itching or maybe even burning. Or you may experience swelling so extreme, it leads to sores. Whether your symptoms are mild or severe, a yeast infection can be uncomfortable. Also known as vaginal candidiasis, yeast infections are caused by a fungus.'],
	['what are the signs of a bad yeast infection?', 'Complications of untreated yeast infections If left untreated, vaginal candidiasis will most likely get worse, causing itching, redness, and inflammation in the area surrounding your vagina. This may lead to a skin infection if the inflamed area becomes cracked, or if continual scratching creates open or raw areas.'],
	['what are the signs of a bad yeast infection?', "Drinking alcohol may also put you at greater risk for yeast infections. So if you're worried about yeast infection symptoms, consider curbing your cocktails. Eating only yeast-free foods is one way some women try to control yeast infections."],
	]
	scores = model.predict(pairs)
	print(scores.shape)
	# (5,)

	# Or rank different texts based on similarity to a single text
	ranks = model.rank(
	'what are the signs of a bad yeast infection?',
	[
	"['Itching and irritation in the vagina and vulva.', 'A burning sensation, especially during intercourse or while urinating.', 'Redness and swelling of the vulva.', 'Vaginal pain and soreness.', 'Vaginal rash.', 'Thick, white, odor-free vaginal discharge with a cottage cheese appearance.', 'Watery vaginal discharge.']",
	'Vaginal yeast infections can cause: itching and irritation in the vagina. redness, swelling, or itching of the vulva (the folds of skin outside the vagina) a thick, white discharge that can look like cottage cheese and is usually odorless, although it might smell like bread or yeast.',
	'It can feel like itching or maybe even burning. Or you may experience swelling so extreme, it leads to sores. Whether your symptoms are mild or severe, a yeast infection can be uncomfortable. Also known as vaginal candidiasis, yeast infections are caused by a fungus.',
	'Complications of untreated yeast infections If left untreated, vaginal candidiasis will most likely get worse, causing itching, redness, and inflammation in the area surrounding your vagina. This may lead to a skin infection if the inflamed area becomes cracked, or if continual scratching creates open or raw areas.',
	"Drinking alcohol may also put you at greater risk for yeast infections. So if you're worried about yeast infection symptoms, consider curbing your cocktails. Eating only yeast-free foods is one way some women try to control yeast infections.",
	]
	)
	# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
	```

	<!--
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	</details>
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	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

	<details><summary>Click to expand</summary>

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	## Evaluation

	### Metrics

	#### Cross Encoder Reranking

	* Dataset: `gooaq-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": 10,
	"always_rerank_positives": false
	}
	```

	\| Metric \| Value \|
	\|:------------\|:---------------------\|
	\| map \| 0.7451 (+0.2140) \|
	\| mrr@10 \| 0.7443 (+0.2203) \|
	\| ndcg@10 \| 0.7849 (+0.1937) \|

	#### Cross Encoder Reranking

	* Dataset: `gooaq-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": 10,
	"always_rerank_positives": true
	}
	```

	\| Metric \| Value \|
	\|:------------\|:---------------------\|
	\| map \| 0.8039 (+0.2728) \|
	\| mrr@10 \| 0.8028 (+0.2788) \|
	\| ndcg@10 \| 0.8475 (+0.2563) \|

	<!--
	## 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: 578,402 training samples
	* Columns: <code>question</code>, <code>answer</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| question \| answer \| label \|
	\|:--------\|:-----------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------\|:------------------------------------------------\|
	\| type \| string \| string \| int \|
	\| details \| <ul><li>min: 21 characters</li><li>mean: 43.81 characters</li><li>max: 91 characters</li></ul> \| <ul><li>min: 51 characters</li><li>mean: 251.2 characters</li><li>max: 365 characters</li></ul> \| <ul><li>0: ~82.90%</li><li>1: ~17.10%</li></ul> \|
	* Samples:
	\| question \| answer \| label \|
	\|:----------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------\|
	\| <code>what are the signs of a bad yeast infection?</code> \| <code>['Itching and irritation in the vagina and vulva.', 'A burning sensation, especially during intercourse or while urinating.', 'Redness and swelling of the vulva.', 'Vaginal pain and soreness.', 'Vaginal rash.', 'Thick, white, odor-free vaginal discharge with a cottage cheese appearance.', 'Watery vaginal discharge.']</code> \| <code>1</code> \|
	\| <code>what are the signs of a bad yeast infection?</code> \| <code>Vaginal yeast infections can cause: itching and irritation in the vagina. redness, swelling, or itching of the vulva (the folds of skin outside the vagina) a thick, white discharge that can look like cottage cheese and is usually odorless, although it might smell like bread or yeast.</code> \| <code>0</code> \|
	\| <code>what are the signs of a bad yeast infection?</code> \| <code>It can feel like itching or maybe even burning. Or you may experience swelling so extreme, it leads to sores. Whether your symptoms are mild or severe, a yeast infection can be uncomfortable. Also known as vaginal candidiasis, yeast infections are caused by a fungus.</code> \| <code>0</code> \|
	* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
	```json
	{
	"activation_fct": "torch.nn.modules.linear.Identity",
	"pos_weight": 5
	}
	```

	### Training Hyperparameters
	#### Non-Default Hyperparameters

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 64
	- `per_device_eval_batch_size`: 64
	- `learning_rate`: 2e-05
	- `num_train_epochs`: 1
	- `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`: 64
	- `per_device_eval_batch_size`: 64
	- `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`: 1
	- `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
	- `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
	- `average_tokens_across_devices`: False
	- `prompts`: None
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| gooaq-dev_ndcg@10 \|
	\|:----------:\|:--------:\|:-------------:\|:--------------------:\|
	\| -1 \| -1 \| - \| 0.1489 (-0.4423) \|
	\| 0.0001 \| 1 \| 1.328 \| - \|
	\| 0.0221 \| 200 \| 1.1586 \| - \|
	\| 0.0443 \| 400 \| 0.7765 \| - \|
	\| 0.0664 \| 600 \| 0.651 \| - \|
	\| 0.0885 \| 800 \| 0.6165 \| - \|
	\| 0.1106 \| 1000 \| 0.6434 \| 0.7674 (+0.1762) \|
	\| 0.1328 \| 1200 \| 0.5952 \| - \|
	\| 0.1549 \| 1400 \| 0.573 \| - \|
	\| 0.1770 \| 1600 \| 0.5538 \| - \|
	\| 0.1992 \| 1800 \| 0.5492 \| - \|
	\| 0.2213 \| 2000 \| 0.5452 \| 0.8095 (+0.2182) \|
	\| 0.2434 \| 2200 \| 0.5325 \| - \|
	\| 0.2655 \| 2400 \| 0.5178 \| - \|
	\| 0.2877 \| 2600 \| 0.5233 \| - \|
	\| 0.3098 \| 2800 \| 0.5079 \| - \|
	\| 0.3319 \| 3000 \| 0.5084 \| 0.8178 (+0.2266) \|
	\| 0.3541 \| 3200 \| 0.5104 \| - \|
	\| 0.3762 \| 3400 \| 0.5053 \| - \|
	\| 0.3983 \| 3600 \| 0.4892 \| - \|
	\| 0.4204 \| 3800 \| 0.4879 \| - \|
	\| 0.4426 \| 4000 \| 0.4969 \| 0.8260 (+0.2348) \|
	\| 0.4647 \| 4200 \| 0.492 \| - \|
	\| 0.4868 \| 4400 \| 0.4798 \| - \|
	\| 0.5090 \| 4600 \| 0.4708 \| - \|
	\| 0.5311 \| 4800 \| 0.4638 \| - \|
	\| 0.5532 \| 5000 \| 0.4746 \| 0.8286 (+0.2374) \|
	\| 0.5753 \| 5200 \| 0.4467 \| - \|
	\| 0.5975 \| 5400 \| 0.4615 \| - \|
	\| 0.6196 \| 5600 \| 0.452 \| - \|
	\| 0.6417 \| 5800 \| 0.4632 \| - \|
	\| 0.6639 \| 6000 \| 0.4517 \| 0.8290 (+0.2378) \|
	\| 0.6860 \| 6200 \| 0.447 \| - \|
	\| 0.7081 \| 6400 \| 0.4581 \| - \|
	\| 0.7303 \| 6600 \| 0.4521 \| - \|
	\| 0.7524 \| 6800 \| 0.4461 \| - \|
	\| 0.7745 \| 7000 \| 0.4418 \| 0.8372 (+0.2459) \|
	\| 0.7966 \| 7200 \| 0.4279 \| - \|
	\| 0.8188 \| 7400 \| 0.4136 \| - \|
	\| 0.8409 \| 7600 \| 0.4163 \| - \|
	\| 0.8630 \| 7800 \| 0.4099 \| - \|
	\| 0.8852 \| 8000 \| 0.4156 \| 0.8431 (+0.2518) \|
	\| 0.9073 \| 8200 \| 0.4146 \| - \|
	\| 0.9294 \| 8400 \| 0.4264 \| - \|
	\| 0.9515 \| 8600 \| 0.4261 \| - \|
	\| 0.9737 \| 8800 \| 0.4145 \| - \|
	\| 0.9958 \| 9000 \| 0.4219 \| 0.8475 (+0.2562) \|
	\| -1 \| -1 \| - \| 0.8475 (+0.2562) \|

	* The bold row denotes the saved checkpoint.

	### Framework Versions
	- Python: 3.11.10
	- Sentence Transformers: 3.5.0.dev0
	- Transformers: 4.49.0
	- PyTorch: 2.5.1+cu124
	- Accelerate: 1.2.0
	- Datasets: 2.21.0
	- Tokenizers: 0.21.0

	## 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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