---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:10053
- loss:MultipleNegativesRankingLoss
base_model: answerdotai/ModernBERT-base
widget:
- source_sentence: Fluorescence quenching of tryptophan residues
sentences:
- 'Fluorescence of buried tyrosine residues in proteins. '
- 'A fluorescence quenching study of tryptophanyl residues of (Ca2+ + Mg2+)-ATPase
from sarcoplasmic reticulum. '
- 'Some hormonal influences on the acetylation of sulfanilamide in vivo. '
- source_sentence: Human migration to the Americas
sentences:
- 'Homo sapiens in the Americas. Overview of the earliest human expansion in the
New World. '
- 'Profiles of College Drinkers Defined by Alcohol Behaviors at the Week Level:
Replication Across Semesters and Prospective Associations With Hazardous Drinking
and Dependence-Related Symptoms. '
- 'Human migration. '
- source_sentence: Human Mobility Prediction
sentences:
- 'Human mobility prediction from region functions with taxi trajectories. '
- 'Understanding Human Mobility from Twitter. '
- 'Ovarian cancer gene therapy using HPV-16 pseudovirion carrying the HSV-tk gene. '
- source_sentence: Nevirapine Resistance
sentences:
- 'Nevirapine toxicity. '
- 'Recognizing rhenium. '
- 'Update on nevirapine: quest for a niche. '
- source_sentence: EHL tendon reconstruction
sentences:
- 'A Combined Surgical Approach for Extensor Hallucis Longus Reconstruction: Two
Case Reports. '
- 'Flexor tendon reconstruction. '
- 'Noble gases and neuroprotection: summary of current evidence. '
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: SentenceTransformer based on answerdotai/ModernBERT-base
results:
- task:
type: triplet
name: Triplet
dataset:
name: triplet dev
type: triplet-dev
metrics:
- type: cosine_accuracy
value: 0.887
name: Cosine Accuracy
---
# 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 json 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)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- json
### 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': 512, '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 = [
'EHL tendon reconstruction',
'A Combined Surgical Approach for Extensor Hallucis Longus Reconstruction: Two Case Reports. ',
'Flexor tendon reconstruction. ',
]
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]
```
## Evaluation
### Metrics
#### Triplet
* Dataset: `triplet-dev`
* Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:--------------------|:----------|
| **cosine_accuracy** | **0.887** |
## Training Details
### Training Dataset
#### json
* Dataset: json
* Size: 10,053 training samples
* Columns: anchor, positive, and negative
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | - min: 4 tokens
- mean: 8.86 tokens
- max: 34 tokens
| - min: 4 tokens
- mean: 21.84 tokens
- max: 62 tokens
| - min: 3 tokens
- mean: 13.65 tokens
- max: 50 tokens
|
* Samples:
| anchor | positive | negative |
|:-------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------|
| COM-induced secretome changes in U937 monocytes | Characterization of calcium oxalate crystal-induced changes in the secretome of U937 human monocytes. | Monocytes. |
| Metamaterials | Sound attenuation optimization using metaporous materials tuned on exceptional points. | Metamaterials: A cat's eye for all directions. |
| Pediatric Parasitology | Parasitic infections among school age children 6 to 11-years-of-age in the Eastern province. | [DIALOGUE ON PEDIATRIC PARASITOLOGY]. |
* Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 0.0002
- `num_train_epochs`: 2
- `lr_scheduler_type`: cosine_with_restarts
- `warmup_ratio`: 0.1
- `bf16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
Click to expand
- `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`: 0.0002
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: cosine_with_restarts
- `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`: 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
Click to expand
| Epoch | Step | Training Loss | triplet-dev_cosine_accuracy |
|:------:|:----:|:-------------:|:---------------------------:|
| 0 | 0 | - | 0.457 |
| 0.0189 | 1 | 5.2934 | - |
| 0.0377 | 2 | 5.2413 | - |
| 0.0566 | 3 | 4.9969 | - |
| 0.0755 | 4 | 4.5579 | - |
| 0.0943 | 5 | 3.9145 | - |
| 0.1132 | 6 | 3.3775 | - |
| 0.1321 | 7 | 2.8787 | - |
| 0.1509 | 8 | 3.0147 | - |
| 0.1698 | 9 | 2.7166 | - |
| 0.1887 | 10 | 2.7875 | - |
| 0.2075 | 11 | 2.3848 | - |
| 0.2264 | 12 | 2.1921 | - |
| 0.2453 | 13 | 1.7009 | - |
| 0.2642 | 14 | 1.7649 | - |
| 0.2830 | 15 | 1.7948 | - |
| 0.3019 | 16 | 1.5384 | - |
| 0.3208 | 17 | 1.6039 | - |
| 0.3396 | 18 | 1.3364 | - |
| 0.3585 | 19 | 1.3852 | - |
| 0.3774 | 20 | 1.2427 | - |
| 0.3962 | 21 | 1.3216 | - |
| 0.4151 | 22 | 1.4202 | - |
| 0.4340 | 23 | 1.2754 | - |
| 0.4528 | 24 | 1.281 | - |
| 0.4717 | 25 | 1.1709 | 0.815 |
| 0.4906 | 26 | 1.2363 | - |
| 0.5094 | 27 | 1.2169 | - |
| 0.5283 | 28 | 1.1495 | - |
| 0.5472 | 29 | 1.0066 | - |
| 0.5660 | 30 | 1.0478 | - |
| 0.5849 | 31 | 1.1511 | - |
| 0.6038 | 32 | 0.9992 | - |
| 0.6226 | 33 | 1.095 | - |
| 0.6415 | 34 | 1.1699 | - |
| 0.6604 | 35 | 0.9866 | - |
| 0.6792 | 36 | 1.1303 | - |
| 0.6981 | 37 | 1.1126 | - |
| 0.7170 | 38 | 0.889 | - |
| 0.7358 | 39 | 1.0355 | - |
| 0.7547 | 40 | 1.0129 | - |
| 0.7736 | 41 | 1.118 | - |
| 0.7925 | 42 | 0.8494 | - |
| 0.8113 | 43 | 1.0829 | - |
| 0.8302 | 44 | 0.8751 | - |
| 0.8491 | 45 | 0.8115 | - |
| 0.8679 | 46 | 0.8579 | - |
| 0.8868 | 47 | 1.1111 | - |
| 0.9057 | 48 | 0.9032 | - |
| 0.9245 | 49 | 1.0394 | - |
| 0.9434 | 50 | 0.9691 | 0.862 |
| 0.9623 | 51 | 1.023 | - |
| 0.9811 | 52 | 0.9465 | - |
| 1.0 | 53 | 0.6713 | - |
| 1.0189 | 54 | 0.9773 | - |
| 1.0377 | 55 | 0.8693 | - |
| 1.0566 | 56 | 0.7187 | - |
| 1.0755 | 57 | 0.805 | - |
| 1.0943 | 58 | 0.728 | - |
| 1.1132 | 59 | 1.0967 | - |
| 1.1321 | 60 | 0.7036 | - |
| 1.1509 | 61 | 0.8213 | - |
| 1.1698 | 62 | 0.57 | - |
| 1.1887 | 63 | 0.7006 | - |
| 1.2075 | 64 | 0.5091 | - |
| 1.2264 | 65 | 0.5758 | - |
| 1.2453 | 66 | 0.4484 | - |
| 1.2642 | 67 | 0.397 | - |
| 1.2830 | 68 | 0.6172 | - |
| 1.3019 | 69 | 0.513 | - |
| 1.3208 | 70 | 0.4447 | - |
| 1.3396 | 71 | 0.3205 | - |
| 1.3585 | 72 | 0.5881 | - |
| 1.3774 | 73 | 0.2543 | - |
| 1.3962 | 74 | 0.3648 | - |
| 1.4151 | 75 | 0.4849 | 0.876 |
| 1.4340 | 76 | 0.3455 | - |
| 1.4528 | 77 | 0.3424 | - |
| 1.4717 | 78 | 0.224 | - |
| 1.4906 | 79 | 0.18 | - |
| 1.5094 | 80 | 0.2255 | - |
| 1.5283 | 81 | 0.3024 | - |
| 1.5472 | 82 | 0.1835 | - |
| 1.5660 | 83 | 0.1946 | - |
| 1.5849 | 84 | 0.1958 | - |
| 1.6038 | 85 | 0.1568 | - |
| 1.6226 | 86 | 0.1626 | - |
| 1.6415 | 87 | 0.1774 | - |
| 1.6604 | 88 | 0.1934 | - |
| 1.6792 | 89 | 0.2426 | - |
| 1.6981 | 90 | 0.2958 | - |
| 1.7170 | 91 | 0.1606 | - |
| 1.7358 | 92 | 0.2281 | - |
| 1.7547 | 93 | 0.1786 | - |
| 1.7736 | 94 | 0.2241 | - |
| 1.7925 | 95 | 0.1909 | - |
| 1.8113 | 96 | 0.236 | - |
| 1.8302 | 97 | 0.1332 | - |
| 1.8491 | 98 | 0.1247 | - |
| 1.8679 | 99 | 0.156 | - |
| 1.8868 | 100 | 0.2152 | 0.889 |
| 1.9057 | 101 | 0.1549 | - |
| 1.9245 | 102 | 0.2226 | - |
| 1.9434 | 103 | 0.21 | - |
| 1.9623 | 104 | 0.2139 | - |
| 1.9811 | 105 | 0.1864 | - |
| 2.0 | 106 | 0.0719 | 0.887 |