README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:9984
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: python to dict if only one item
  sentences:
  - "def get_from_gnucash26_date(date_str: str) -> date:\n    \"\"\" Creates a datetime\
    \ from GnuCash 2.6 date string \"\"\"\n    date_format = \"%Y%m%d\"\n    result\
    \ = datetime.strptime(date_str, date_format).date()\n    return result"
  - "def multidict_to_dict(d):\n    \"\"\"\n    Turns a werkzeug.MultiDict or django.MultiValueDict\
    \ into a dict with\n    list values\n    :param d: a MultiDict or MultiValueDict\
    \ instance\n    :return: a dict instance\n    \"\"\"\n    return dict((k, v[0]\
    \ if len(v) == 1 else v) for k, v in iterlists(d))"
  - "def wipe_table(self, table: str) -> int:\n        \"\"\"Delete all records from\
    \ a table. Use caution!\"\"\"\n        sql = \"DELETE FROM \" + self.delimit(table)\n\
    \        return self.db_exec(sql)"
- source_sentence: how to add a string to a filename in python
  sentences:
  - "def html_to_text(content):\n    \"\"\" Converts html content to plain text \"\
    \"\"\n    text = None\n    h2t = html2text.HTML2Text()\n    h2t.ignore_links =\
    \ False\n    text = h2t.handle(content)\n    return text"
  - "def _get_column_by_db_name(cls, name):\n        \"\"\"\n        Returns the column,\
    \ mapped by db_field name\n        \"\"\"\n        return cls._columns.get(cls._db_map.get(name,\
    \ name))"
  - "def add_suffix(fullname, suffix):\n    \"\"\" Add suffix to a full file name\"\
    \"\"\n    name, ext = os.path.splitext(fullname)\n    return name + '_' + suffix\
    \ + ext"
- source_sentence: human readable string of object python
  sentences:
  - "def pretty(obj, verbose=False, max_width=79, newline='\\n'):\n    \"\"\"\n  \
    \  Pretty print the object's representation.\n    \"\"\"\n    stream = StringIO()\n\
    \    printer = RepresentationPrinter(stream, verbose, max_width, newline)\n  \
    \  printer.pretty(obj)\n    printer.flush()\n    return stream.getvalue()"
  - "def asMaskedArray(self):\n        \"\"\" Creates converts to a masked array\n\
    \        \"\"\"\n        return ma.masked_array(data=self.data, mask=self.mask,\
    \ fill_value=self.fill_value)"
  - "def list_depth(list_, func=max, _depth=0):\n    \"\"\"\n    Returns the deepest\
    \ level of nesting within a list of lists\n\n    Args:\n       list_  : a nested\
    \ listlike object\n       func   : depth aggregation strategy (defaults to max)\n\
    \       _depth : internal var\n\n    Example:\n        >>> # ENABLE_DOCTEST\n\
    \        >>> from utool.util_list import *  # NOQA\n        >>> list_ = [[[[[1]]],\
    \ [3]], [[1], [3]], [[1], [3]]]\n        >>> result = (list_depth(list_, _depth=0))\n\
    \        >>> print(result)\n\n    \"\"\"\n    depth_list = [list_depth(item, func=func,\
    \ _depth=_depth + 1)\n                  for item in  list_ if util_type.is_listlike(item)]\n\
    \    if len(depth_list) > 0:\n        return func(depth_list)\n    else:\n   \
    \     return _depth"
- source_sentence: python parse query param
  sentences:
  - "def read_las(source, closefd=True):\n    \"\"\" Entry point for reading las data\
    \ in pylas\n\n    Reads the whole file into memory.\n\n    >>> las = read_las(\"\
    pylastests/simple.las\")\n    >>> las.classification\n    array([1, 1, 1, ...,\
    \ 1, 1, 1], dtype=uint8)\n\n    Parameters\n    ----------\n    source : str or\
    \ io.BytesIO\n        The source to read data from\n\n    closefd: bool\n    \
    \        if True and the source is a stream, the function will close it\n    \
    \        after it is done reading\n\n\n    Returns\n    -------\n    pylas.lasdatas.base.LasBase\n\
    \        The object you can interact with to get access to the LAS points & VLRs\n\
    \    \"\"\"\n    with open_las(source, closefd=closefd) as reader:\n        return\
    \ reader.read()"
  - "def parse_query_string(query):\n    \"\"\"\n    parse_query_string:\n    very\
    \ simplistic. won't do the right thing with list values\n    \"\"\"\n    result\
    \ = {}\n    qparts = query.split('&')\n    for item in qparts:\n        key, value\
    \ = item.split('=')\n        key = key.strip()\n        value = value.strip()\n\
    \        result[key] = unquote_plus(value)\n    return result"
  - "def _clean_dict(target_dict, whitelist=None):\n    \"\"\" Convenience function\
    \ that removes a dicts keys that have falsy values\n    \"\"\"\n    assert isinstance(target_dict,\
    \ dict)\n    return {\n        ustr(k).strip(): ustr(v).strip()\n        for k,\
    \ v in target_dict.items()\n        if v not in (None, Ellipsis, [], (), \"\"\
    )\n        and (not whitelist or k in whitelist)\n    }"
- source_sentence: python automatic figure out encoding
  sentences:
  - "def get_best_encoding(stream):\n    \"\"\"Returns the default stream encoding\
    \ if not found.\"\"\"\n    rv = getattr(stream, 'encoding', None) or sys.getdefaultencoding()\n\
    \    if is_ascii_encoding(rv):\n        return 'utf-8'\n    return rv"
  - "def is_natural(x):\n    \"\"\"A non-negative integer.\"\"\"\n    try:\n     \
    \   is_integer = int(x) == x\n    except (TypeError, ValueError):\n        return\
    \ False\n    return is_integer and x >= 0"
  - "def _tool_to_dict(tool):\n    \"\"\"Parse a tool definition into a cwl2wdl style\
    \ dictionary.\n    \"\"\"\n    out = {\"name\": _id_to_name(tool.tool[\"id\"]),\n\
    \           \"baseCommand\": \" \".join(tool.tool[\"baseCommand\"]),\n       \
    \    \"arguments\": [],\n           \"inputs\": [_input_to_dict(i) for i in tool.tool[\"\
    inputs\"]],\n           \"outputs\": [_output_to_dict(o) for o in tool.tool[\"\
    outputs\"]],\n           \"requirements\": _requirements_to_dict(tool.requirements\
    \ + tool.hints),\n           \"stdin\": None, \"stdout\": None}\n    return out"
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/huggingface/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("Narekatsy/fine-tuned-cosqa")
# Run inference
sentences = [
    'python automatic figure out encoding',
    'def get_best_encoding(stream):\n    """Returns the default stream encoding if not found."""\n    rv = getattr(stream, \'encoding\', None) or sys.getdefaultencoding()\n    if is_ascii_encoding(rv):\n        return \'utf-8\'\n    return rv',
    'def _tool_to_dict(tool):\n    """Parse a tool definition into a cwl2wdl style dictionary.\n    """\n    out = {"name": _id_to_name(tool.tool["id"]),\n           "baseCommand": " ".join(tool.tool["baseCommand"]),\n           "arguments": [],\n           "inputs": [_input_to_dict(i) for i in tool.tool["inputs"]],\n           "outputs": [_output_to_dict(o) for o in tool.tool["outputs"]],\n           "requirements": _requirements_to_dict(tool.requirements + tool.hints),\n           "stdin": None, "stdout": None}\n    return out',
]
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,  0.6173,  0.1376],
#         [ 0.6173,  1.0000, -0.0456],
#         [ 0.1376, -0.0456,  1.0000]])
```

<!--
### 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.*
-->

<!--
## 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: 9,984 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                       | sentence_1                                                                          |
  |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                              |
  | details | <ul><li>min: 6 tokens</li><li>mean: 9.69 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 39 tokens</li><li>mean: 87.33 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
  | sentence_0                                              | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                   |
  |:--------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>how to zip files to directory in python</code>    | <code>def unzip_file_to_dir(path_to_zip, output_directory):<br>    """<br>    Extract a ZIP archive to a directory<br>    """<br>    z = ZipFile(path_to_zip, 'r')<br>    z.extractall(output_directory)<br>    z.close()</code>                                                                                                                                                                                             |
  | <code>mnist multi gpu training python tensorflow</code> | <code>def transformer_tall_pretrain_lm_tpu_adafactor():<br>  """Hparams for transformer on LM pretraining (with 64k vocab) on TPU."""<br>  hparams = transformer_tall_pretrain_lm()<br>  update_hparams_for_tpu(hparams)<br>  hparams.max_length = 1024<br>  # For multi-problem on TPU we need it in absolute examples.<br>  hparams.batch_size = 8<br>  hparams.multiproblem_vocab_size = 2**16<br>  return hparams</code> |
  | <code>get file name without extension in python</code>  | <code>def remove_ext(fname):<br>    """Removes the extension from a filename<br>    """<br>    bn = os.path.basename(fname)<br>    return os.path.splitext(bn)[0]</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`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 2
- `multi_dataset_batch_sampler`: round_robin

#### 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`: 32
- `per_device_eval_batch_size`: 32
- `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
- `num_train_epochs`: 2
- `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
- `bf16`: False
- `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}
- `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
- `project`: huggingface
- `trackio_space_id`: trackio
- `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`: no
- `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`: True
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
- `router_mapping`: {}
- `learning_rate_mapping`: {}

</details>

### Training Logs
| Epoch  | Step | Training Loss |
|:------:|:----:|:-------------:|
| 1.6026 | 500  | 0.1512        |


### Framework Versions
- Python: 3.11.3
- Sentence Transformers: 5.1.2
- Transformers: 4.57.1
- PyTorch: 2.9.0+cpu
- Accelerate: 1.11.0
- Datasets: 4.4.1
- 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}
}
```

<!--
## 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.*
-->