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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:100006
- loss:CachedMultipleNegativesRankingLoss
base_model: answerdotai/ModernBERT-base
widget:
- source_sentence: how much weight can you lose in a week healthy?
sentences:
- Biology
- 'Summary: According to experts, losing 1–2 pounds (0.45–0.9 kg) per week is a
healthy and safe rate, while losing more than this is considered too fast. However,
you may lose more than that during your first week of an exercise or diet plan.'
- The number of valence electrons is the number of electrons in the outer shell,
that the atom uses for bonding. Nitrogen has 5 electrons in its n=2 (outer) shell.
- source_sentence: how long after having a baby can i get a tattoo?
sentences:
- It is suggested that mothers wait at least until 9-12 months after birth, when
the child is no longer dependent solely on breastmilk before getting a tattoo.
Reputable tattoo artists will have a waiver for the client to sign that asks about
pregnancy and breastfeeding.
- Medicine
- Americans on average are down to 44 gallons of soda per year, and up to about
58 gallons of water. That's 7,242 ounces of water annually -- 20 ounces daily,
which is 2.5 cups.
- source_sentence: is all uhmw anti static?
sentences:
- The bacteria Streptococcus pyogenes causes it. It's most common in infants and
children, but it frequently occurs in teenagers and adults as well. It causes
white streaks or spots in the throat.
- Chemistry
- UHMW is available in a special anti-static grade that helps protect against EsD
(static discharge) or to help keep dust and particles from building up on the
product surface. The anti-static additives are built-in so the anti-static properties
will last throughout the life of the material.
- source_sentence: is closing cost tax deductible?
sentences:
- Medicine
- 1 tablespoon (tbsp) of granulated sugar equals to 12.5998 grams (g) in granulated
sugar mass.
- In general, the only settlement or closing costs you can deduct are home mortgage
interest and certain real estate taxes. You deduct them in the year you buy your
home if you itemize your deductions. ... See IRS Publication 530, "Tax Information
for Homeowners" and look for "Settlement or closing costs" for more details.
- source_sentence: what is the connection between cancer and the cell cycle?
sentences:
- Biology
- Conclusion. Cancer is unchecked cell growth. Mutations in genes can cause cancer
by accelerating cell division rates or inhibiting normal controls on the system,
such as cell cycle arrest or programmed cell death. As a mass of cancerous cells
grows, it can develop into a tumor.
- Your vomit may appear black if the blood has been oxidized by the acids in your
stomach. The iron in your blood turns brown to black with time. Since the blood
is no longer bright red, it means that the bleeding has either stopped or is only
happening in a small amount.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on answerdotai/ModernBERT-base
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: cosine_accuracy@1
value: 0.1
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.18
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.24
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.34
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.1
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.06
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.04800000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.034
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.1
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.15
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.21
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.31
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.19343658524041285
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.16590476190476192
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.17642959153410534
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: cosine_accuracy@1
value: 0.12
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.28
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.4
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.52
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.12
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.09333333333333332
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.08
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.052000000000000005
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.12
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.28
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.4
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.52
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2984940860938879
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2304365079365079
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.24691442502099614
name: Cosine Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: cosine_accuracy@1
value: 0.11
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.23
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.32
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.43000000000000005
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.11
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.07666666666666666
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.064
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.043000000000000003
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.11
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.21500000000000002
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.305
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.41500000000000004
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.24596533566715037
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.1981706349206349
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.21167200827755073
name: Cosine Map@100
---
# SentenceTransformer based on answerdotai/ModernBERT-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the csv dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) <!-- at revision 5756c58a31a2478f9e62146021f48295a92c3da5 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- csv
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## 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 SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'what is the connection between cancer and the cell cycle?',
'Conclusion. Cancer is unchecked cell growth. Mutations in genes can cause cancer by accelerating cell division rates or inhibiting normal controls on the system, such as cell cycle arrest or programmed cell death. As a mass of cancerous cells grows, it can develop into a tumor.',
'Biology',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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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>
</details>
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### Out-of-Scope Use
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## Evaluation
### Metrics
#### Information Retrieval
* Datasets: `NanoNQ` and `NanoMSMARCO`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | NanoNQ | NanoMSMARCO |
|:--------------------|:-----------|:------------|
| cosine_accuracy@1 | 0.1 | 0.12 |
| cosine_accuracy@3 | 0.18 | 0.28 |
| cosine_accuracy@5 | 0.24 | 0.4 |
| cosine_accuracy@10 | 0.34 | 0.52 |
| cosine_precision@1 | 0.1 | 0.12 |
| cosine_precision@3 | 0.06 | 0.0933 |
| cosine_precision@5 | 0.048 | 0.08 |
| cosine_precision@10 | 0.034 | 0.052 |
| cosine_recall@1 | 0.1 | 0.12 |
| cosine_recall@3 | 0.15 | 0.28 |
| cosine_recall@5 | 0.21 | 0.4 |
| cosine_recall@10 | 0.31 | 0.52 |
| **cosine_ndcg@10** | **0.1934** | **0.2985** |
| cosine_mrr@10 | 0.1659 | 0.2304 |
| cosine_map@100 | 0.1764 | 0.2469 |
#### Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator)
| Metric | Value |
|:--------------------|:----------|
| cosine_accuracy@1 | 0.11 |
| cosine_accuracy@3 | 0.23 |
| cosine_accuracy@5 | 0.32 |
| cosine_accuracy@10 | 0.43 |
| cosine_precision@1 | 0.11 |
| cosine_precision@3 | 0.0767 |
| cosine_precision@5 | 0.064 |
| cosine_precision@10 | 0.043 |
| cosine_recall@1 | 0.11 |
| cosine_recall@3 | 0.215 |
| cosine_recall@5 | 0.305 |
| cosine_recall@10 | 0.415 |
| **cosine_ndcg@10** | **0.246** |
| cosine_mrr@10 | 0.1982 |
| cosine_map@100 | 0.2117 |
<!--
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## Training Details
### Training Dataset
#### csv
* Dataset: csv
* Size: 100,006 training samples
* Columns: <code>question</code>, <code>answer</code>, and <code>category</code>
* Approximate statistics based on the first 1000 samples:
| | question | answer | category |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 11.91 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 57.49 tokens</li><li>max: 136 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 4.0 tokens</li><li>max: 4 tokens</li></ul> |
* Samples:
| question | answer | category |
|:---------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------|
| <code>how many times a week should you use heat on your hair?</code> | <code>Don't style hair with heat every day. Hot tools can also make hair look crispy and create split ends if overused. Blow out hair 3-5 times a week and try to limit your flat iron/curling iron usage to 1-2 times a week.”</code> | <code>Medicine</code> |
| <code>do african violets like to be root bound?</code> | <code>African violets only bloom when they're root bound. When it is time to repot, be sure to use an organic potting soil made specifically for African violets, such as Espoma's African Violet Mix. They flower best in small pots — choose one that's about a third of the diameter of their leaf spread.</code> | <code>Biology</code> |
| <code>is pgwp exempt from lmia?</code> | <code>The PGWP is exempt from Labour Market Impact Assessment (LMIA) requirements. The candidate must have attended a recognized post-secondary school, or a secondary school that offers qualifying programs, for at least eight months.</code> | <code>Medicine</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `learning_rate`: 0.0001
- `num_train_epochs`: 1
- `warmup_ratio`: 0.05
- `bf16`: True
- `batch_sampler`: no_duplicates
#### 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`: 256
- `per_device_eval_batch_size`: 256
- `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`: 0.0001
- `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`: 0.05
- `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`: 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`: 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}
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | NanoNQ_cosine_ndcg@10 | NanoMSMARCO_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:---------------------:|:--------------------------:|:----------------------------:|
| 0 | 0 | - | 0.0388 | 0.0863 | 0.0626 |
| 0.0763 | 10 | 0.5482 | - | - | - |
| 0.1527 | 20 | 0.1079 | - | - | - |
| 0.2290 | 30 | 0.1491 | - | - | - |
| 0.3053 | 40 | 0.1381 | - | - | - |
| 0.3817 | 50 | 0.0873 | 0.0909 | 0.2197 | 0.1553 |
| 0.4580 | 60 | 0.133 | - | - | - |
| 0.5344 | 70 | 0.0539 | - | - | - |
| 0.6107 | 80 | 0.029 | - | - | - |
| 0.6870 | 90 | 0.0008 | - | - | - |
| 0.7634 | 100 | 0.0997 | 0.1982 | 0.2657 | 0.2320 |
| 0.8397 | 110 | 0.04 | - | - | - |
| 0.9160 | 120 | 0.0053 | - | - | - |
| 0.9924 | 130 | 0.0095 | - | - | - |
| 1.0 | 131 | - | 0.1934 | 0.2985 | 0.2460 |
### Framework Versions
- Python: 3.12.3
- Sentence Transformers: 3.3.1
- Transformers: 4.48.0.dev0
- PyTorch: 2.5.1
- Accelerate: 1.2.1
- Datasets: 3.2.0
- 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",
}
```
#### CachedMultipleNegativesRankingLoss
```bibtex
@misc{gao2021scaling,
title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
year={2021},
eprint={2101.06983},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
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