---
base_model: allenai/specter2_base
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:9988
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Splenomegaly in Malta fever
sentences:
- 'TROPICAL SPLENOMEGALY. '
- '[Voluminous migrating spleen in the course of Malta fever: effects of splenectomy]. '
- '[Adenoma of appendix]. '
- source_sentence: sRNA regulation
sentences:
- 'SR proteins control a complex network of RNA-processing events. '
- 'Convergence of submodality-specific input onto neurons in primary somatosensory
cortex. '
- 'Dynamic features of gene expression control by small regulatory RNAs. '
- source_sentence: Foley catheter hysterosalpingography
sentences:
- 'Hysterosalpingography using a Foley catheter. '
- '[Long-term follow-up of adult patients with isolated congenital AV block]. '
- 'Hysterosalpingography. '
- source_sentence: Anti-endoglin monoclonal antibodies
sentences:
- 'Cortisol response to general anaesthesia for medical imaging in children. '
- 'Anti-endoglin monoclonal antibodies are effective for suppressing metastasis
and the primary tumors by targeting tumor vasculature. '
- 'Endoglin: Beyond the Endothelium. '
- source_sentence: Alternariol Methyl Ether Quantitation
sentences:
- 'Stable isotope dilution assays of alternariol and alternariol monomethyl ether
in beverages. '
- 'The roles of eotaxin and the STAT6 signalling pathway in eosinophil recruitment
and host resistance to the nematodes Nippostrongylus brasiliensis and Heligmosomoides
bakeri. '
- 'Mechanisms of Action and Toxicity of the Mycotoxin Alternariol: A Review. '
---
# SentenceTransformer based on allenai/specter2_base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [allenai/specter2_base](https://huggingface.co/allenai/specter2_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:** [allenai/specter2_base](https://huggingface.co/allenai/specter2_base)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **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: BertModel
(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 = [
'Alternariol Methyl Ether Quantitation',
'Stable isotope dilution assays of alternariol and alternariol monomethyl ether in beverages. ',
'Mechanisms of Action and Toxicity of the Mycotoxin Alternariol: A Review. ',
]
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]
```
## Training Details
### Training Dataset
#### json
* Dataset: json
* Size: 9,988 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: 7.66 tokens
- max: 34 tokens
| - min: 6 tokens
- mean: 19.05 tokens
- max: 42 tokens
| - min: 4 tokens
- mean: 11.84 tokens
- max: 48 tokens
|
* Samples:
| anchor | positive | negative |
|:-----------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------|
| mechanotransduction pathways | Signalling cascades in mechanotransduction: cell-matrix interactions and mechanical loading. | Mechanotransduction: May the force be with you. |
| FSR-tunable comb filter | Multiwavelength Raman fiber laser with a continuously-tunable spacing. | Tunable multiwavelength fiber laser using a comb filter based on erbium-ytterbium co-doped polarization maintaining fiber loop mirror. |
| Radiation pneumonitis enhancement | Induction and concurrent taxanes enhance both the pulmonary metabolic radiation response and the radiation pneumonitis response in patients with esophagus cancer. | Imaging of Hypersensitivity Pneumonitis. |
* 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
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `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`: no
- `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`: 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`: 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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
Click to expand
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.0095 | 1 | 2.9432 |
| 0.0190 | 2 | 3.0121 |
| 0.0286 | 3 | 2.9051 |
| 0.0381 | 4 | 2.7906 |
| 0.0476 | 5 | 2.6592 |
| 0.0571 | 6 | 2.2835 |
| 0.0667 | 7 | 2.1373 |
| 0.0762 | 8 | 1.7872 |
| 0.0857 | 9 | 1.6329 |
| 0.0952 | 10 | 1.5184 |
| 0.1048 | 11 | 1.234 |
| 0.1143 | 12 | 1.0315 |
| 0.1238 | 13 | 0.9664 |
| 0.1333 | 14 | 0.9369 |
| 0.1429 | 15 | 0.6871 |
| 0.1524 | 16 | 0.5633 |
| 0.1619 | 17 | 0.5141 |
| 0.1714 | 18 | 0.5259 |
| 0.1810 | 19 | 0.4295 |
| 0.1905 | 20 | 0.4585 |
| 0.2 | 21 | 0.2799 |
| 0.2095 | 22 | 0.4226 |
| 0.2190 | 23 | 0.2524 |
| 0.2286 | 24 | 0.2135 |
| 0.2381 | 25 | 0.1958 |
| 0.2476 | 26 | 0.1823 |
| 0.2571 | 27 | 0.393 |
| 0.2667 | 28 | 0.3186 |
| 0.2762 | 29 | 0.1414 |
| 0.2857 | 30 | 0.1927 |
| 0.2952 | 31 | 0.2597 |
| 0.3048 | 32 | 0.1291 |
| 0.3143 | 33 | 0.1488 |
| 0.3238 | 34 | 0.1203 |
| 0.3333 | 35 | 0.2001 |
| 0.3429 | 36 | 0.1877 |
| 0.3524 | 37 | 0.0713 |
| 0.3619 | 38 | 0.1778 |
| 0.3714 | 39 | 0.1179 |
| 0.3810 | 40 | 0.147 |
| 0.3905 | 41 | 0.1158 |
| 0.4 | 42 | 0.1003 |
| 0.4095 | 43 | 0.158 |
| 0.4190 | 44 | 0.159 |
| 0.4286 | 45 | 0.063 |
| 0.4381 | 46 | 0.1309 |
| 0.4476 | 47 | 0.0327 |
| 0.4571 | 48 | 0.1665 |
| 0.4667 | 49 | 0.1064 |
| 0.4762 | 50 | 0.0699 |
| 0.4857 | 51 | 0.0674 |
| 0.4952 | 52 | 0.0508 |
| 0.5048 | 53 | 0.0493 |
| 0.5143 | 54 | 0.0565 |
| 0.5238 | 55 | 0.0366 |
| 0.5333 | 56 | 0.0606 |
| 0.5429 | 57 | 0.0727 |
| 0.5524 | 58 | 0.092 |
| 0.5619 | 59 | 0.0628 |
| 0.5714 | 60 | 0.0369 |
| 0.5810 | 61 | 0.0889 |
| 0.5905 | 62 | 0.0409 |
| 0.6 | 63 | 0.0545 |
| 0.6095 | 64 | 0.0856 |
| 0.6190 | 65 | 0.0478 |
| 0.6286 | 66 | 0.0584 |
| 0.6381 | 67 | 0.0757 |
| 0.6476 | 68 | 0.0609 |
| 0.6571 | 69 | 0.0381 |
| 0.6667 | 70 | 0.069 |
| 0.6762 | 71 | 0.0243 |
| 0.6857 | 72 | 0.0517 |
| 0.6952 | 73 | 0.0332 |
| 0.7048 | 74 | 0.0662 |
| 0.7143 | 75 | 0.0753 |
| 0.7238 | 76 | 0.0914 |
| 0.7333 | 77 | 0.1094 |
| 0.7429 | 78 | 0.0557 |
| 0.7524 | 79 | 0.0436 |
| 0.7619 | 80 | 0.0137 |
| 0.7714 | 81 | 0.0399 |
| 0.7810 | 82 | 0.0278 |
| 0.7905 | 83 | 0.0438 |
| 0.8 | 84 | 0.1392 |
| 0.8095 | 85 | 0.0299 |
| 0.8190 | 86 | 0.0667 |
| 0.8286 | 87 | 0.0404 |
| 0.8381 | 88 | 0.0166 |
| 0.8476 | 89 | 0.1679 |
| 0.8571 | 90 | 0.0282 |
| 0.8667 | 91 | 0.0628 |
| 0.8762 | 92 | 0.0618 |
| 0.8857 | 93 | 0.0167 |
| 0.8952 | 94 | 0.2108 |
| 0.9048 | 95 | 0.0749 |
| 0.9143 | 96 | 0.0997 |
| 0.9238 | 97 | 0.0675 |
| 0.9333 | 98 | 0.0409 |
| 0.9429 | 99 | 0.0355 |
| 0.9524 | 100 | 0.1391 |
| 0.9619 | 101 | 0.0938 |
| 0.9714 | 102 | 0.0526 |
| 0.9810 | 103 | 0.0035 |
| 0.9905 | 104 | 0.0022 |
| 1.0 | 105 | 0.0016 |