Hugging Face's logo

juanpablomesa
/
bge-small-bioasq-1epochs-batch32

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:4012
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
bge-small-bioasq-1epochs-batch32 / README.md
juanpablomesa's picture
juanpablomesa
Add new SentenceTransformer model.
8a2a3f1 verified
preview code
|
raw
history blame contribute delete
23.4 kB
---
base_model: BAAI/bge-small-en-v1.5
datasets: []
language:
- en
library_name: sentence-transformers
license: apache-2.0
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
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:4012
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 'Extensive messenger RNA editing generates transcript and protein
diversity in genes involved in neural excitability, as previously described, as
well as in genes participating in a broad range of other cellular functions. '
sentences:
- Do cephalopods use RNA editing less frequently than other species?
- GV1001 vaccine targets which enzyme?
- Which event results in the acetylation of S6K1?
- source_sentence: Yes, exposure to household furry pets influences the gut microbiota
of infants.
sentences:
- Can pets affect infant microbiomed?
- What is the mode of action of Thiazovivin?
- What are the effects of CAMK4 inhibition?
- source_sentence: "In children with heart failure evidence of the effect of enalapril\
\ is empirical. Enalapril was clinically safe and effective in 50% to 80% of for\
\ children with cardiac failure secondary to congenital heart malformations before\
\ and after cardiac surgery, impaired ventricular function , valvar regurgitation,\
\ congestive cardiomyopathy, , arterial hypertension, life-threatening arrhythmias\
\ coexisting with circulatory insufficiency. \nACE inhibitors have shown a transient\
\ beneficial effect on heart failure due to anticancer drugs and possibly a beneficial\
\ effect in muscular dystrophy-associated cardiomyopathy, which deserves further\
\ studies."
sentences:
- Which receptors can be evaluated with the [18F]altanserin?
- In what proportion of children with heart failure has Enalapril been shown to
be safe and effective?
- Which major signaling pathways are regulated by RIP1?
- source_sentence: Cellular senescence-associated heterochromatic foci (SAHFS) are
a novel type of chromatin condensation involving alterations of linker histone
H1 and linker DNA-binding proteins. SAHFS can be formed by a variety of cell types,
but their mechanism of action remains unclear.
sentences:
- What is the relationship between the X chromosome and a neutrophil drumstick?
- Which microRNAs are involved in exercise adaptation?
- How are SAHFS created?
- source_sentence: Multicluster Pcdh diversity is required for mouse olfactory neural
circuit assembly. The vertebrate clustered protocadherin (Pcdh) cell surface proteins
are encoded by three closely linked gene clusters (Pcdhα, Pcdhβ, and Pcdhγ). Although
deletion of individual Pcdh clusters had subtle phenotypic consequences, the loss
of all three clusters (tricluster deletion) led to a severe axonal arborization
defect and loss of self-avoidance.
sentences:
- What are the effects of the deletion of all three Pcdh clusters (tricluster deletion)
in mice?
- what is the role of MEF-2 in cardiomyocyte differentiation?
- How many periods of regulatory innovation led to the evolution of vertebrates?
model-index:
- name: BGE small finetuned BIOASQ
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: BAAI/bge small en v1.5
type: BAAI/bge-small-en-v1.5
metrics:
- type: cosine_accuracy@1
value: 0.8415841584158416
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.925035360678925
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.942008486562942
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.958981612446959
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8415841584158416
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.30834512022630833
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.18840169731258838
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09589816124469587
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8415841584158416
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.925035360678925
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.942008486562942
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.958981612446959
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9047357964584107
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.886919916481444
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8877807671526188
name: Cosine Map@100
---
# BGE small finetuned BIOASQ
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5). It maps sentences & paragraphs to a 384-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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0
### 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': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## 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("juanpablomesa/bge-small-bioasq-1epochs-batch32")
# Run inference
sentences = [
'Multicluster Pcdh diversity is required for mouse olfactory neural circuit assembly. The vertebrate clustered protocadherin (Pcdh) cell surface proteins are encoded by three closely linked gene clusters (Pcdhα, Pcdhβ, and Pcdhγ). Although deletion of individual Pcdh clusters had subtle phenotypic consequences, the loss of all three clusters (tricluster deletion) led to a severe axonal arborization defect and loss of self-avoidance.',
'What are the effects of the deletion of all three Pcdh clusters (tricluster deletion) in mice?',
'How many periods of regulatory innovation led to the evolution of vertebrates?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### 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
#### Information Retrieval
* Dataset: `BAAI/bge-small-en-v1.5`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.8416 |
| cosine_accuracy@3 | 0.925 |
| cosine_accuracy@5 | 0.942 |
| cosine_accuracy@10 | 0.959 |
| cosine_precision@1 | 0.8416 |
| cosine_precision@3 | 0.3083 |
| cosine_precision@5 | 0.1884 |
| cosine_precision@10 | 0.0959 |
| cosine_recall@1 | 0.8416 |
| cosine_recall@3 | 0.925 |
| cosine_recall@5 | 0.942 |
| cosine_recall@10 | 0.959 |
| cosine_ndcg@10 | 0.9047 |
| cosine_mrr@10 | 0.8869 |
| **cosine_map@100** | **0.8878** |
<!--
## 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: 4,012 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 63.38 tokens</li><li>max: 485 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.13 tokens</li><li>max: 49 tokens</li></ul> |
* Samples:
| positive | anchor |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|
| <code>Aberrant patterns of H3K4, H3K9, and H3K27 histone lysine methylation were shown to result in histone code alterations, which induce changes in gene expression, and affect the proliferation rate of cells in medulloblastoma.</code> | <code>What is the implication of histone lysine methylation in medulloblastoma?</code> |
| <code>STAG1/STAG2 proteins are tumour suppressor proteins that suppress cell proliferation and are essential for differentiation.</code> | <code>What is the role of STAG1/STAG2 proteins in differentiation?</code> |
| <code>The association between cell phone use and incident glioblastoma remains unclear. Some studies have reported that cell phone use was associated with incident glioblastoma, and with reduced survival of patients diagnosed with glioblastoma. However, other studies have repeatedly replicated to find an association between cell phone use and glioblastoma.</code> | <code>What is the association between cell phone use and glioblastoma?</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](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`: 32
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: 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`: 32
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_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`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: 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}
- `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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | BAAI/bge-small-en-v1.5_cosine_map@100 |
|:------:|:----:|:-------------:|:-------------------------------------:|
| 0.0794 | 10 | 0.5344 | - |
| 0.1587 | 20 | 0.4615 | - |
| 0.2381 | 30 | 0.301 | - |
| 0.3175 | 40 | 0.2169 | - |
| 0.3968 | 50 | 0.1053 | - |
| 0.4762 | 60 | 0.1432 | - |
| 0.5556 | 70 | 0.1589 | - |
| 0.6349 | 80 | 0.1458 | - |
| 0.7143 | 90 | 0.1692 | - |
| 0.7937 | 100 | 0.1664 | - |
| 0.8730 | 110 | 0.1252 | - |
| 0.9524 | 120 | 0.1243 | - |
| 1.0 | 126 | - | 0.8858 |
| 0.0794 | 10 | 0.1393 | - |
| 0.1587 | 20 | 0.1504 | - |
| 0.2381 | 30 | 0.1009 | - |
| 0.3175 | 40 | 0.0689 | - |
| 0.3968 | 50 | 0.0301 | - |
| 0.4762 | 60 | 0.0647 | - |
| 0.5556 | 70 | 0.0748 | - |
| 0.6349 | 80 | 0.0679 | - |
| 0.7143 | 90 | 0.1091 | - |
| 0.7937 | 100 | 0.0953 | - |
| 0.8730 | 110 | 0.089 | - |
| 0.9524 | 120 | 0.0758 | - |
| 1.0 | 126 | - | 0.8878 |
| 0.0794 | 10 | 0.092 | - |
| 0.1587 | 20 | 0.0748 | - |
| 0.2381 | 30 | 0.0392 | - |
| 0.3175 | 40 | 0.014 | - |
| 0.3968 | 50 | 0.0057 | - |
| 0.4762 | 60 | 0.0208 | - |
| 0.5556 | 70 | 0.0173 | - |
| 0.6349 | 80 | 0.0195 | - |
| 0.7143 | 90 | 0.0349 | - |
| 0.7937 | 100 | 0.0483 | - |
| 0.8730 | 110 | 0.0254 | - |
| 0.9524 | 120 | 0.0325 | - |
| 1.0 | 126 | - | 0.8883 |
| 1.0317 | 130 | 0.0582 | - |
| 1.1111 | 140 | 0.0475 | - |
| 1.1905 | 150 | 0.0325 | - |
| 1.2698 | 160 | 0.0058 | - |
| 1.3492 | 170 | 0.0054 | - |
| 1.4286 | 180 | 0.0047 | - |
| 1.5079 | 190 | 0.0076 | - |
| 1.5873 | 200 | 0.0091 | - |
| 1.6667 | 210 | 0.0232 | - |
| 1.7460 | 220 | 0.0147 | - |
| 1.8254 | 230 | 0.0194 | - |
| 1.9048 | 240 | 0.0186 | - |
| 1.9841 | 250 | 0.0141 | - |
| 2.0 | 252 | - | 0.8857 |
| 2.0635 | 260 | 0.037 | - |
| 2.1429 | 270 | 0.0401 | - |
| 2.2222 | 280 | 0.0222 | - |
| 2.3016 | 290 | 0.0134 | - |
| 2.3810 | 300 | 0.008 | - |
| 2.4603 | 310 | 0.0199 | - |
| 2.5397 | 320 | 0.017 | - |
| 2.6190 | 330 | 0.0164 | - |
| 2.6984 | 340 | 0.0344 | - |
| 2.7778 | 350 | 0.0352 | - |
| 2.8571 | 360 | 0.0346 | - |
| 2.9365 | 370 | 0.0256 | - |
| 3.0 | 378 | - | 0.8868 |
| 0.7937 | 100 | 0.0064 | 0.8878 |
### Framework Versions
- Python: 3.11.5
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.31.0
- Datasets: 2.19.1
- Tokenizers: 0.19.1
## 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",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## 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.*
-->