README.md

---
base_model: intfloat/multilingual-e5-base
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:100
- loss:TripletLoss
widget:
- source_sentence: How many athletes from region 151 have won a medal?
  sentences:
  - athletes refer to person_id; region 151 refers to region_id = 151; won a medal
    refers to medal_id <> 4;
  - Rio de Janeiro refers to city_name = 'Rio de Janeiro';
  - the highest number of participants refers to MAX(COUNT(person_id)); the lowest
    number of participants refers to MIN(COUNT(person_id)); Which summer Olympic refers
    to games_name where season = 'Summer';
- source_sentence: What is the id of Rio de Janeiro?
  sentences:
  - year refers to games_year;
  - athletes refer to person_id; region 151 refers to region_id = 151; won a medal
    refers to medal_id <> 4;
  - Rio de Janeiro refers to city_name = 'Rio de Janeiro';
- source_sentence: Please list the Asian populations of all the residential areas
    with the bad alias "URB San Joaquin".
  sentences:
  - '"URB San Joaquin" is the bad_alias'
  - name of congressman implies full name which refers to first_name, last_name; Guanica
    is the city;
  - '"URB San Joaquin" is the bad_alias'
- source_sentence: State the male population for all zip code which were under the
    Berlin, NH CBSA.
  sentences:
  - '"Berlin, NH" is the CBSA_name'
  - '"Barre, VT" is the CBSA_name'
  - representative's full names refer to first_name, last_name; area which has highest
    population in 2020 refers to MAX(population_2020);
- source_sentence: Which state has the most bad aliases?
  sentences:
  - '"York" is the city; ''ME'' is the state; type refers to CBSA_type'
  - the most bad aliases refer to MAX(COUNT(bad_alias));
  - precise location refers to latitude, longitude
---

# SentenceTransformer based on intfloat/multilingual-e5-base

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) on the train and test datasets. 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:** [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) <!-- at revision d13f1b27baf31030b7fd040960d60d909913633f -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
    - train
    - test
<!-- - **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': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (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})
  (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("DariaaaS/e5-args-1")
# Run inference
sentences = [
    'Which state has the most bad aliases?',
    'the most bad aliases refer to MAX(COUNT(bad_alias));',
    'precise location refers to latitude, longitude',
]
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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## Training Details

### Training Datasets

#### train

* Dataset: train
* Size: 80 training samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | query                                                                              | positive                                                                          | negative                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            | string                                                                            |
  | details | <ul><li>min: 11 tokens</li><li>mean: 19.75 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 18.12 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 28.56 tokens</li><li>max: 54 tokens</li></ul> |
* Samples:
  | query                                                | positive                                  | negative                                                                                                |
  |:-----------------------------------------------------|:------------------------------------------|:--------------------------------------------------------------------------------------------------------|
  | <code>How many zip codes are under Barre, VT?</code> | <code>"Barre, VT" is the CBSA_name</code> | <code>coordinates refers to latitude, longitude; latitude = '18.090875; longitude = '-66.867756'</code> |
  | <code>How many zip codes are under Barre, VT?</code> | <code>"Barre, VT" is the CBSA_name</code> | <code>name of county refers to county</code>                                                            |
  | <code>How many zip codes are under Barre, VT?</code> | <code>"Barre, VT" is the CBSA_name</code> | <code>median age over 40 refers to median_age > 40</code>                                               |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
  ```json
  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }
  ```

#### test

* Dataset: test
* Size: 20 training samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | query                                                                             | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 11 tokens</li><li>mean: 12.5 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 22.5 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 23.45 tokens</li><li>max: 56 tokens</li></ul> |
* Samples:
  | query                                                    | positive                                                     | negative                                                                                                       |
  |:---------------------------------------------------------|:-------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------|
  | <code>Where is competitor Estelle Nze Minko from?</code> | <code>Where competitor is from refers to region_name;</code> | <code>NOC code refers to noc; the heaviest refers to MAX(weight);</code>                                       |
  | <code>Where is competitor Estelle Nze Minko from?</code> | <code>Where competitor is from refers to region_name;</code> | <code>host city refers to city_name; the 1968 Winter Olympic Games refer to games_name = '1968 Winter';</code> |
  | <code>Where is competitor Estelle Nze Minko from?</code> | <code>Where competitor is from refers to region_name;</code> | <code>the gold medal refers to medal_name = 'Gold';</code>                                                     |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
  ```json
  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }
  ```

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

- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1
- `fp16`: True

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `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`: 4
- `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
- `eval_on_start`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.4.0+cu121
- Accelerate: 0.32.1
- Datasets: 2.21.0
- 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",
}
```

#### TripletLoss
```bibtex
@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification}, 
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}
```

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