README.md

---
language:
- en
license: apache-2.0
library_name: sentence-transformers
datasets:
- davanstrien/similarity-dataset-sc2-8b
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:n<1K
- loss:MultipleNegativesRankingLoss
base_model: microsoft/mpnet-base
metrics:
- cosine_accuracy
- dot_accuracy
- manhattan_accuracy
- euclidean_accuracy
- max_accuracy
widget:
- source_sentence: Write a Python function that counts the number of even numbers
    in a given list of integers or floats
  sentences:
  - Write a Python function that returns the number of even numbers in a list.
  - Create a Python function that adds up all the numbers in a given list. The function
    should support lists containing only positive integers.
  - Write a Python function that converts a JSON string into a Python dictionary using
    the json module and returns it.
- source_sentence: Develop a Python function to validate whether a given string represents
    a valid IPv4 address or not.
  sentences:
  - Create a Python function to validate a string `s` as an IPv4 address. The function
    should return `True` if `s` is a valid IPv4 address, and `False` otherwise.
  - Write a Python function to find the key with the highest value in a dictionary.
    The function should return the value of the key if it exists
  - Write a Python function that, given a dictionary `d` and an integer `k`, returns
    the sum of the values of the first `k` keys in `d`.
- source_sentence: Write a Python function to create a list of numbers with exactly
    one even number and n-1 odd numbers
  sentences:
  - Write a Python function that returns the number of even numbers in a list.
  - Write a Python function that recursively traverses a given folder structure and
    returns the absolute path of all files that end with ".txt".
  - Write a Python decorator function that overrides the docstring of the decorated
    function, and stores the old docstring and other metadata in a `_doc_metadata`
    attribute of the function.
- source_sentence: 'Implement a Python function that prints the first character of
    a string using its indexing feature. '
  sentences:
  - Write a Python function that takes a string as a parameter and returns the first
    character of the string.
  - Write a Python function that checks if the bit at position `bit` is set in the
    given `integer`. This function should return a boolean value.
  - 'Write a Python function `floor_division(x: int, y: int) -> int` that divides
    two integers `x` and `y` and returns the largest whole number less than or equal
    to the result.'
- source_sentence: Write a Python function that takes a MIDI note number and returns
    the corresponding piano key number.
  sentences:
  - Create a Python function that translates MIDI note numbers into piano key numbers,
    facilitating music generation.
  - Write a Python function that accepts a dictionary and returns a set of distinct
    values. If a key maps to an empty list, return an empty set.
  - Write a Python function `join_strings_with_comma(lst)` that takes a list of strings
    and returns a single string with all the strings from the list, separated by commas.
pipeline_tag: sentence-similarity
co2_eq_emissions:
  emissions: 2.213004168952992
  energy_consumed: 0.006336878829164133
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: Intel(R) Xeon(R) CPU @ 2.20GHz
  ram_total_size: 62.804237365722656
  hours_used: 0.049
  hardware_used: 1 x NVIDIA L4
model-index:
- name: MPNet base trained on AllNLI triplets
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: code similarity dev
      type: code-similarity-dev
    metrics:
    - type: cosine_accuracy
      value: 0.934010152284264
      name: Cosine Accuracy
    - type: dot_accuracy
      value: 0.07106598984771574
      name: Dot Accuracy
    - type: manhattan_accuracy
      value: 0.934010152284264
      name: Manhattan Accuracy
    - type: euclidean_accuracy
      value: 0.9390862944162437
      name: Euclidean Accuracy
    - type: max_accuracy
      value: 0.9390862944162437
      name: Max Accuracy
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: cosine_accuracy
      value: 0.934010152284264
      name: Cosine Accuracy
    - type: dot_accuracy
      value: 0.07106598984771574
      name: Dot Accuracy
    - type: manhattan_accuracy
      value: 0.934010152284264
      name: Manhattan Accuracy
    - type: euclidean_accuracy
      value: 0.9390862944162437
      name: Euclidean Accuracy
    - type: max_accuracy
      value: 0.9390862944162437
      name: Max Accuracy
---

# MPNet base trained on AllNLI triplets

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base). 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:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
- **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': False}) with Transformer model: MPNetModel 
  (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("davanstrien/code-prompt-similarity-model")
# Run inference
sentences = [
    'Write a Python function that takes a MIDI note number and returns the corresponding piano key number.',
    'Create a Python function that translates MIDI note numbers into piano key numbers, facilitating music generation.',
    'Write a Python function that accepts a dictionary and returns a set of distinct values. If a key maps to an empty list, return an empty set.',
]
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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<details><summary>Click to expand</summary>

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## Evaluation

### Metrics

#### Triplet
* Dataset: `code-similarity-dev`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric             | Value      |
|:-------------------|:-----------|
| cosine_accuracy    | 0.934      |
| dot_accuracy       | 0.0711     |
| manhattan_accuracy | 0.934      |
| euclidean_accuracy | 0.9391     |
| **max_accuracy**   | **0.9391** |

#### Triplet

* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric             | Value      |
|:-------------------|:-----------|
| cosine_accuracy    | 0.934      |
| dot_accuracy       | 0.0711     |
| manhattan_accuracy | 0.934      |
| euclidean_accuracy | 0.9391     |
| **max_accuracy**   | **0.9391** |

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## Training Details

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 10
- `warmup_ratio`: 0.1
- `bf16`: 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`: 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`: 5e-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`: 10
- `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`: 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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch | Step | Training Loss | loss   | code-similarity-dev_max_accuracy | max_accuracy |
|:-----:|:----:|:-------------:|:------:|:--------------------------------:|:------------:|
| 0     | 0    | -             | -      | 0.8680                           | -            |
| 2.0   | 100  | 0.6379        | 0.1845 | 0.9340                           | -            |
| 4.0   | 200  | 0.0399        | 0.1577 | 0.9543                           | -            |
| 6.0   | 300  | 0.0059        | 0.1577 | 0.9543                           | -            |
| 8.0   | 400  | 0.0018        | 0.1662 | 0.9492                           | -            |
| 10.0  | 500  | 0.0009        | 0.1643 | 0.9391                           | 0.9391       |


### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.006 kWh
- **Carbon Emitted**: 0.002 kg of CO2
- **Hours Used**: 0.049 hours

### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA L4
- **CPU Model**: Intel(R) Xeon(R) CPU @ 2.20GHz
- **RAM Size**: 62.80 GB

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.0
- Transformers: 4.41.1
- PyTorch: 2.3.0+cu121
- Accelerate: 0.30.1
- 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}
}
```

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