README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:9020
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: python multiprocessing show cpu count
  sentences:
  - "def unique(seq):\n    \"\"\"Return the unique elements of a collection even if\
    \ those elements are\n       unhashable and unsortable, like dicts and sets\"\"\
    \"\n    cleaned = []\n    for each in seq:\n        if each not in cleaned:\n\
    \            cleaned.append(each)\n    return cleaned"
  - "def is_in(self, point_x, point_y):\n        \"\"\" Test if a point is within\
    \ this polygonal region \"\"\"\n\n        point_array = array(((point_x, point_y),))\n\
    \        vertices = array(self.points)\n        winding = self.inside_rule ==\
    \ \"winding\"\n        result = points_in_polygon(point_array, vertices, winding)\n\
    \        return result[0]"
  - "def machine_info():\n    \"\"\"Retrieve core and memory information for the current\
    \ machine.\n    \"\"\"\n    import psutil\n    BYTES_IN_GIG = 1073741824.0\n \
    \   free_bytes = psutil.virtual_memory().total\n    return [{\"memory\": float(\"\
    %.1f\" % (free_bytes / BYTES_IN_GIG)), \"cores\": multiprocessing.cpu_count(),\n\
    \             \"name\": socket.gethostname()}]"
- source_sentence: python subplot set the whole title
  sentences:
  - "def set_title(self, title, **kwargs):\n        \"\"\"Sets the title on the underlying\
    \ matplotlib AxesSubplot.\"\"\"\n        ax = self.get_axes()\n        ax.set_title(title,\
    \ **kwargs)"
  - "def moving_average(array, n=3):\n    \"\"\"\n    Calculates the moving average\
    \ of an array.\n\n    Parameters\n    ----------\n    array : array\n        The\
    \ array to have the moving average taken of\n    n : int\n        The number of\
    \ points of moving average to take\n    \n    Returns\n    -------\n    MovingAverageArray\
    \ : array\n        The n-point moving average of the input array\n    \"\"\"\n\
    \    ret = _np.cumsum(array, dtype=float)\n    ret[n:] = ret[n:] - ret[:-n]\n\
    \    return ret[n - 1:] / n"
  - "def to_query_parameters(parameters):\n    \"\"\"Converts DB-API parameter values\
    \ into query parameters.\n\n    :type parameters: Mapping[str, Any] or Sequence[Any]\n\
    \    :param parameters: A dictionary or sequence of query parameter values.\n\n\
    \    :rtype: List[google.cloud.bigquery.query._AbstractQueryParameter]\n    :returns:\
    \ A list of query parameters.\n    \"\"\"\n    if parameters is None:\n      \
    \  return []\n\n    if isinstance(parameters, collections_abc.Mapping):\n    \
    \    return to_query_parameters_dict(parameters)\n\n    return to_query_parameters_list(parameters)"
- source_sentence: python merge two set to dict
  sentences:
  - "def make_regex(separator):\n    \"\"\"Utility function to create regexp for matching\
    \ escaped separators\n    in strings.\n\n    \"\"\"\n    return re.compile(r'(?:'\
    \ + re.escape(separator) + r')?((?:[^' +\n                      re.escape(separator)\
    \ + r'\\\\]|\\\\.)+)')"
  - "def csvtolist(inputstr):\n    \"\"\" converts a csv string into a list \"\"\"\
    \n    reader = csv.reader([inputstr], skipinitialspace=True)\n    output = []\n\
    \    for r in reader:\n        output += r\n    return output"
  - "def dict_merge(set1, set2):\n    \"\"\"Joins two dictionaries.\"\"\"\n    return\
    \ dict(list(set1.items()) + list(set2.items()))"
- source_sentence: python string % substitution float
  sentences:
  - "def _configure_logger():\n    \"\"\"Configure the logging module.\"\"\"\n   \
    \ if not app.debug:\n        _configure_logger_for_production(logging.getLogger())\n\
    \    elif not app.testing:\n        _configure_logger_for_debugging(logging.getLogger())"
  - "def __set__(self, instance, value):\n        \"\"\" Set a related object for\
    \ an instance. \"\"\"\n\n        self.map[id(instance)] = (weakref.ref(instance),\
    \ value)"
  - "def format_float(value): # not used\n    \"\"\"Modified form of the 'g' format\
    \ specifier.\n    \"\"\"\n    string = \"{:g}\".format(value).replace(\"e+\",\
    \ \"e\")\n    string = re.sub(\"e(-?)0*(\\d+)\", r\"e\\1\\2\", string)\n    return\
    \ string"
- source_sentence: bottom 5 rows in python
  sentences:
  - "def refresh(self, document):\n\t\t\"\"\" Load a new copy of a document from the\
    \ database.  does not\n\t\t\treplace the old one \"\"\"\n\t\ttry:\n\t\t\told_cache_size\
    \ = self.cache_size\n\t\t\tself.cache_size = 0\n\t\t\tobj = self.query(type(document)).filter_by(mongo_id=document.mongo_id).one()\n\
    \t\tfinally:\n\t\t\tself.cache_size = old_cache_size\n\t\tself.cache_write(obj)\n\
    \t\treturn obj"
  - "def table_top_abs(self):\n        \"\"\"Returns the absolute position of table\
    \ top\"\"\"\n        table_height = np.array([0, 0, self.table_full_size[2]])\n\
    \        return string_to_array(self.floor.get(\"pos\")) + table_height"
  - "def get_dimension_array(array):\n    \"\"\"\n    Get dimension of an array getting\
    \ the number of rows and the max num of\n    columns.\n    \"\"\"\n    if all(isinstance(el,\
    \ list) for el in array):\n        result = [len(array), len(max([x for x in array],\
    \ key=len,))]\n\n    # elif array and isinstance(array, list):\n    else:\n  \
    \      result = [len(array), 1]\n\n    return result"
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). 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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **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': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, '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("Devy1/MiniLM-cosqa-512")
# Run inference
sentences = [
    'bottom 5 rows in python',
    'def table_top_abs(self):\n        """Returns the absolute position of table top"""\n        table_height = np.array([0, 0, self.table_full_size[2]])\n        return string_to_array(self.floor.get("pos")) + table_height',
    'def refresh(self, document):\n\t\t""" Load a new copy of a document from the database.  does not\n\t\t\treplace the old one """\n\t\ttry:\n\t\t\told_cache_size = self.cache_size\n\t\t\tself.cache_size = 0\n\t\t\tobj = self.query(type(document)).filter_by(mongo_id=document.mongo_id).one()\n\t\tfinally:\n\t\t\tself.cache_size = old_cache_size\n\t\tself.cache_write(obj)\n\t\treturn obj',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.4537, -0.0817],
#         [ 0.4537,  1.0000, -0.0463],
#         [-0.0817, -0.0463,  1.0000]])
```

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You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 9,020 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                           | positive                                                                            |
  |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                              |
  | details | <ul><li>min: 6 tokens</li><li>mean: 9.67 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 40 tokens</li><li>mean: 86.17 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
  | anchor                                                  | positive                                                                                                                                                                                                                                                                                                                                                                                                                      |
  |:--------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>1d array in char datatype in python</code>        | <code>def _convert_to_array(array_like, dtype):<br>        """<br>        Convert Matrix attributes which are array-like or buffer to array.<br>        """<br>        if isinstance(array_like, bytes):<br>            return np.frombuffer(array_like, dtype=dtype)<br>        return np.asarray(array_like, dtype=dtype)</code>                                                                                            |
  | <code>python condition non none</code>                  | <code>def _not(condition=None, **kwargs):<br>    """<br>    Return the opposite of input condition.<br><br>    :param condition: condition to process.<br><br>    :result: not condition.<br>    :rtype: bool<br>    """<br><br>    result = True<br><br>    if condition is not None:<br>        result = not run(condition, **kwargs)<br><br>    return result</code>                                                       |
  | <code>accessing a column from a matrix in python</code> | <code>def get_column(self, X, column):<br>        """Return a column of the given matrix.<br><br>        Args:<br>            X: `numpy.ndarray` or `pandas.DataFrame`.<br>            column: `int` or `str`.<br><br>        Returns:<br>            np.ndarray: Selected column.<br>        """<br>        if isinstance(X, pd.DataFrame):<br>            return X[column].values<br><br>        return X[:, column]</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",
      "gather_across_devices": false
  }
  ```

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

- `per_device_train_batch_size`: 512
- `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`: 512
- `per_device_eval_batch_size`: 8
- `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`: 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`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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}
- `parallelism_config`: 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`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `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
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}

</details>

### Training Logs
| Epoch  | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.0556 | 1    | 1.2259        |
| 0.1111 | 2    | 1.1589        |
| 0.1667 | 3    | 0.9588        |
| 0.2222 | 4    | 1.0265        |
| 0.2778 | 5    | 0.9783        |
| 0.3333 | 6    | 0.9464        |
| 0.3889 | 7    | 0.9527        |
| 0.4444 | 8    | 0.969         |
| 0.5    | 9    | 1.0237        |
| 0.5556 | 10   | 1.0134        |
| 0.6111 | 11   | 0.9297        |
| 0.6667 | 12   | 0.9877        |
| 0.7222 | 13   | 0.9531        |
| 0.7778 | 14   | 0.9156        |
| 0.8333 | 15   | 0.8613        |
| 0.8889 | 16   | 0.83          |
| 0.9444 | 17   | 0.8991        |
| 1.0    | 18   | 0.6764        |
| 1.0556 | 19   | 0.8545        |
| 1.1111 | 20   | 0.7454        |
| 1.1667 | 21   | 0.834         |
| 1.2222 | 22   | 0.7625        |
| 1.2778 | 23   | 0.7808        |
| 1.3333 | 24   | 0.817         |
| 1.3889 | 25   | 0.8032        |
| 1.4444 | 26   | 0.7854        |
| 1.5    | 27   | 0.7376        |
| 1.5556 | 28   | 0.8346        |
| 1.6111 | 29   | 0.8738        |
| 1.6667 | 30   | 0.7524        |
| 1.7222 | 31   | 0.72          |
| 1.7778 | 32   | 0.711         |
| 1.8333 | 33   | 0.7498        |
| 1.8889 | 34   | 0.7597        |
| 1.9444 | 35   | 0.7883        |
| 2.0    | 36   | 0.5038        |
| 2.0556 | 37   | 0.6932        |
| 2.1111 | 38   | 0.7273        |
| 2.1667 | 39   | 0.6723        |
| 2.2222 | 40   | 0.7059        |
| 2.2778 | 41   | 0.6159        |
| 2.3333 | 42   | 0.809         |
| 2.3889 | 43   | 0.6959        |
| 2.4444 | 44   | 0.7881        |
| 2.5    | 45   | 0.6861        |
| 2.5556 | 46   | 0.6545        |
| 2.6111 | 47   | 0.7235        |
| 2.6667 | 48   | 0.7031        |
| 2.7222 | 49   | 0.6679        |
| 2.7778 | 50   | 0.6835        |
| 2.8333 | 51   | 0.6773        |
| 2.8889 | 52   | 0.6972        |
| 2.9444 | 53   | 0.7043        |
| 3.0    | 54   | 0.4647        |


### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 5.1.1
- Transformers: 4.56.2
- PyTorch: 2.8.0+cu128
- Accelerate: 1.10.1
- Datasets: 4.1.1
- Tokenizers: 0.22.1

## 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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