sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:10000 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss en arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:BAAI/bge-base-en-v1.5 base_model:finetune:BAAI/bge-base-en-v1.5 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-base-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_ndcg@100
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:10000
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Enzalutamide ( brand name Xtandi ) is a synthetic non-steroidal
    antiandrogen ( NSAA ) which was developed by the pharmaceutical company Medivation
    for the treatment of metastatic , castration-resistant prostate cancer . Medivation
    has reported up to an 89 % decrease in serum prostate specific antigen ( PSA )
    levels after a month of taking the drug . Research suggests that enzalutamide
    may also be effective in the treatment of certain types of breast cancer . In
    August 2012 , the United States ( U.S. ) Food and Drug Administration ( FDA )
    approved enzalutamide for the treatment of castration-resistant prostate cancer
    .
  sentences:
  - what type of cancer is enzalutamide
  - who is simon cho
  - who is dr william farone
- source_sentence: Sohel Rana is a Bangladeshi footballer who plays as a midfielder
    . He currently plays for Sheikh Jamal Dhanmondi Club .
  sentences:
  - who is sohel rana
  - who is olympicos
  - who is roberto laserna
- source_sentence: Qarah Qayeh ( قره قيه , also Romanized as Qareh Qīyeh ) is a village
    in Chaharduli Rural District , Keshavarz District , Shahin Dezh County , West
    Azerbaijan Province , Iran . At the 2006 census , its population was 465 , in
    93 families .
  sentences:
  - what was the knoxville riot
  - what language is kbif
  - where is qarah qayeh
- source_sentence: Martin Severin Janus From ( 8 April 1828 -- 6 May 1895 ) was a
    Danish chess master .   Born in Nakskov , From received his first education at
    the grammar school of Nykøbing Falster . He entered the army as a volunteer during
    the Prussian-Danish War ( Schleswig-Holstein War of Succession ) , where he served
    in the brigade of Major-General Olaf Rye and partook in the Battle of Fredericia
    on July 6 , 1849 .   After the war From settled in Copenhagen . He was employed
    by the Statistical Bureau , where he met Magnus Oscar Møllerstrøm , then the strongest
    chess player in Copenhagen . Next , he worked in the central office for prison
    management , and in 1890 he became an inspector of the penitentiary of Christianshavn
    . In 1891 he received the order Ridder af Dannebrog ( `` Knight of the Danish
    cloth '' , i.e. flag of Denmark ) , which is the second highest of Danish orders
    .   In 1895 Severin From died of cancer . He is interred at Vestre Cemetery ,
    Copenhagen .
  sentences:
  - when did martin from die
  - what is hymenoxys lemmonii
  - where is macomb square il
- source_sentence: The Recession of 1937 -- 1938 was an economic downturn that occurred
    during the Great Depression in the United States .   By the spring of 1937 , production
    , profits , and wages had regained their 1929 levels . Unemployment remained high
    , but it was slightly lower than the 25 % rate seen in 1933 . The American economy
    took a sharp downturn in mid-1937 , lasting for 13 months through most of 1938
    . Industrial production declined almost 30 percent and production of durable goods
    fell even faster .   Unemployment jumped from 14.3 % in 1937 to 19.0 % in 1938
    . Manufacturing output fell by 37 % from the 1937 peak and was back to 1934 levels
    .  Producers reduced their expenditures on durable goods , and inventories declined
    , but personal income was only 15 % lower than it had been at the peak in 1937
    . In most sectors , hourly earnings continued to rise throughout the recession
    , which partly compensated for the reduction in the number of hours worked . As
    unemployment rose , consumers expenditures declined , thereby leading to further
    cutbacks in production .
  sentences:
  - when did the great depression peak in the u.s. economy?
  - what is tom mount's specialty
  - where is poulton
model-index:
- name: SentenceTransformer based on BAAI/bge-base-en-v1.5
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.9175
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9565
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.965
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.977
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.9175
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.31883333333333325
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19300000000000003
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09770000000000002
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.9175
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9565
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.965
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.977
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9481552613003054
      name: Cosine Ndcg@10
    - type: cosine_ndcg@100
      value: 0.9518775022084042
      name: Cosine Ndcg@100
    - type: cosine_mrr@10
      value: 0.938853373015873
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9396524466438041
      name: Cosine Map@100
---

# SentenceTransformer based on BAAI/bge-base-en-v1.5

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 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': 768, '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("MugheesAwan11/bge-base-climate_fever-dataset-10k-2k-v1")
# Run inference
sentences = [
    'The Recession of 1937 -- 1938 was an economic downturn that occurred during the Great Depression in the United States .   By the spring of 1937 , production , profits , and wages had regained their 1929 levels . Unemployment remained high , but it was slightly lower than the 25 % rate seen in 1933 . The American economy took a sharp downturn in mid-1937 , lasting for 13 months through most of 1938 . Industrial production declined almost 30 percent and production of durable goods fell even faster .   Unemployment jumped from 14.3 % in 1937 to 19.0 % in 1938 . Manufacturing output fell by 37 % from the 1937 peak and was back to 1934 levels .  Producers reduced their expenditures on durable goods , and inventories declined , but personal income was only 15 % lower than it had been at the peak in 1937 . In most sectors , hourly earnings continued to rise throughout the recession , which partly compensated for the reduction in the number of hours worked . As unemployment rose , consumers expenditures declined , thereby leading to further cutbacks in production .',
    'when did the great depression peak in the u.s. economy?',
    'where is poulton',
]
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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## Evaluation

### Metrics

#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.9175     |
| cosine_accuracy@3   | 0.9565     |
| cosine_accuracy@5   | 0.965      |
| cosine_accuracy@10  | 0.977      |
| cosine_precision@1  | 0.9175     |
| cosine_precision@3  | 0.3188     |
| cosine_precision@5  | 0.193      |
| cosine_precision@10 | 0.0977     |
| cosine_recall@1     | 0.9175     |
| cosine_recall@3     | 0.9565     |
| cosine_recall@5     | 0.965      |
| cosine_recall@10    | 0.977      |
| cosine_ndcg@10      | 0.9482     |
| cosine_ndcg@100     | 0.9519     |
| cosine_mrr@10       | 0.9389     |
| **cosine_map@100**  | **0.9397** |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 10,000 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: 2 tokens</li><li>mean: 116.45 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.6 tokens</li><li>max: 19 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | anchor                                   |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------|
  | <code>Professor Maurice Cockrill , RA , FBA ( 8 October 1936 -- 1 December 2013 ) was a British painter and poet .   Born in Hartlepool , County Durham , he studied at Wrexham School of Art , north east Wales , then Denbigh Technical College and later the University of Reading from 1960 -- 64 .  In Liverpool , where he lived for nearly twenty years from 1964 , he taught at Liverpool College of Art and Liverpool Polytechnic . He was a central figure in Liverpool 's artistic life , regularly exhibiting at the Walker Art Gallery , before his departure for London in 1982 .  Cockrill 's Liverpool work was in line with that of John Baum , Sam Walsh and Adrian Henri , employing Pop and Photo-Realist styles , but later he moved towards Romantic Expressionism , as it was shown in his retrospective at the Walker Art Gallery , Liverpool in 1995 .  His poetry was published in magazines such as `` Ambit '' and `` Poetry Review '' .   He was formerly the Keeper of the Royal Academy , and as such managed the RA Schools of the Establishment as well as being a member of the Board and Executive Committee .</code> | <code>who was maurice cockrill</code>    |
  | <code>Nowa Dąbrowa -LSB- ` nowa-dom ` browa -RSB- is a village in the administrative district of Gmina Kwilcz , within Międzychód County , Greater Poland Voivodeship , in west-central Poland . It lies approximately 16 km south-east of Międzychód and 59 km west of the regional capital Poznań .   The village has a population of 40 .</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | <code>where is nowa dbrowa poland</code> |
  | <code>Hymenoxys lemmonii is a species of flowering plant in the daisy family known by the common names Lemmon 's rubberweed , Lemmon 's bitterweed , and alkali hymenoxys . It is native to the western United States in and around the Great Basin in Utah , Nevada , northern California , and southeastern Oregon .   Hymenoxys lemmonii is a biennial or perennial herb with one or more branching stems growing erect to a maximum height near 50 centimeters ( 20 inches ) . It produces straight , dark green leaves up to 9 centimeters ( 3.6 inches ) long and divided into a number of narrow , pointed lobes . The foliage and stem may be hairless to quite woolly . The daisy-like flower head is generally at least 1.5 centimeters ( 0.6 inches ) wide , with a center of 50 -- 125 thick golden disc florets and a shaggy fringe of 9 -- 12 golden ray florets .   The species is named for John Gill Lemmon , husband of prominent American botanist Sarah Plummer Lemmon .</code>                                                                                                                                                      | <code>what is hymenoxys lemmonii</code>  |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768
      ],
      "matryoshka_weights": [
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `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`: cosine
- `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`: True
- `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`: 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_fused
- `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 | dim_768_cosine_map@100 |
|:-------:|:-------:|:-------------:|:----------------------:|
| 0.0319  | 10      | 0.1626        | -                      |
| 0.0639  | 20      | 0.1168        | -                      |
| 0.0958  | 30      | 0.0543        | -                      |
| 0.1278  | 40      | 0.1227        | -                      |
| 0.1597  | 50      | 0.061         | -                      |
| 0.1917  | 60      | 0.0537        | -                      |
| 0.2236  | 70      | 0.0693        | -                      |
| 0.2556  | 80      | 0.1115        | -                      |
| 0.2875  | 90      | 0.0541        | -                      |
| 0.3195  | 100     | 0.0774        | -                      |
| 0.3514  | 110     | 0.0639        | -                      |
| 0.3834  | 120     | 0.0639        | -                      |
| 0.4153  | 130     | 0.0567        | -                      |
| 0.4473  | 140     | 0.0385        | -                      |
| 0.4792  | 150     | 0.0452        | -                      |
| 0.5112  | 160     | 0.0641        | -                      |
| 0.5431  | 170     | 0.042         | -                      |
| 0.5751  | 180     | 0.0243        | -                      |
| 0.6070  | 190     | 0.0405        | -                      |
| 0.6390  | 200     | 0.062         | -                      |
| 0.6709  | 210     | 0.0366        | -                      |
| 0.7029  | 220     | 0.0399        | -                      |
| 0.7348  | 230     | 0.0382        | -                      |
| 0.7668  | 240     | 0.0387        | -                      |
| 0.7987  | 250     | 0.0575        | -                      |
| 0.8307  | 260     | 0.0391        | -                      |
| 0.8626  | 270     | 0.0776        | -                      |
| 0.8946  | 280     | 0.0258        | -                      |
| 0.9265  | 290     | 0.0493        | -                      |
| 0.9585  | 300     | 0.037         | -                      |
| 0.9904  | 310     | 0.0499        | -                      |
| **1.0** | **313** | **-**         | **0.9397**             |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.14
- 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",
}
```

#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning}, 
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

#### 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}
}
```

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