---
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)
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
### 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-256")
# 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, 0.4934, -0.0548],
# [ 0.4934, 1.0000, -0.0408],
# [-0.0548, -0.0408, 1.0000]])
```
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 9,020 training samples
* Columns: anchor and positive
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 6 tokens
- mean: 9.67 tokens
- max: 21 tokens
| - min: 40 tokens
- mean: 86.17 tokens
- max: 256 tokens
|
* Samples:
| anchor | positive |
|:--------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1d array in char datatype in python | def _convert_to_array(array_like, dtype):
"""
Convert Matrix attributes which are array-like or buffer to array.
"""
if isinstance(array_like, bytes):
return np.frombuffer(array_like, dtype=dtype)
return np.asarray(array_like, dtype=dtype) |
| python condition non none | def _not(condition=None, **kwargs):
"""
Return the opposite of input condition.
:param condition: condition to process.
:result: not condition.
:rtype: bool
"""
result = True
if condition is not None:
result = not run(condition, **kwargs)
return result |
| accessing a column from a matrix in python | def get_column(self, X, column):
"""Return a column of the given matrix.
Args:
X: `numpy.ndarray` or `pandas.DataFrame`.
column: `int` or `str`.
Returns:
np.ndarray: Selected column.
"""
if isinstance(X, pd.DataFrame):
return X[column].values
return X[:, column] |
* Loss: [MultipleNegativesRankingLoss](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`: 256
- `fp16`: True
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 256
- `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`: {}
Click to expand
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.0278 | 1 | 0.8774 |
| 0.0556 | 2 | 0.6553 |
| 0.0833 | 3 | 0.7565 |
| 0.1111 | 4 | 0.7703 |
| 0.1389 | 5 | 0.5969 |
| 0.1667 | 6 | 0.5905 |
| 0.1944 | 7 | 0.76 |
| 0.2222 | 8 | 0.6663 |
| 0.25 | 9 | 0.625 |
| 0.2778 | 10 | 0.5882 |
| 0.3056 | 11 | 0.623 |
| 0.3333 | 12 | 0.5631 |
| 0.3611 | 13 | 0.524 |
| 0.3889 | 14 | 0.7467 |
| 0.4167 | 15 | 0.6272 |
| 0.4444 | 16 | 0.5395 |
| 0.4722 | 17 | 0.6429 |
| 0.5 | 18 | 0.6462 |
| 0.5278 | 19 | 0.6576 |
| 0.5556 | 20 | 0.6333 |
| 0.5833 | 21 | 0.6013 |
| 0.6111 | 22 | 0.5671 |
| 0.6389 | 23 | 0.6835 |
| 0.6667 | 24 | 0.5734 |
| 0.6944 | 25 | 0.5969 |
| 0.7222 | 26 | 0.5446 |
| 0.75 | 27 | 0.6675 |
| 0.7778 | 28 | 0.5319 |
| 0.8056 | 29 | 0.5374 |
| 0.8333 | 30 | 0.5085 |
| 0.8611 | 31 | 0.6267 |
| 0.8889 | 32 | 0.4322 |
| 0.9167 | 33 | 0.5383 |
| 0.9444 | 34 | 0.5712 |
| 0.9722 | 35 | 0.5485 |
| 1.0 | 36 | 0.214 |
| 1.0278 | 37 | 0.515 |
| 1.0556 | 38 | 0.4593 |
| 1.0833 | 39 | 0.4891 |
| 1.1111 | 40 | 0.3927 |
| 1.1389 | 41 | 0.4909 |
| 1.1667 | 42 | 0.4875 |
| 1.1944 | 43 | 0.4611 |
| 1.2222 | 44 | 0.409 |
| 1.25 | 45 | 0.4307 |
| 1.2778 | 46 | 0.4946 |
| 1.3056 | 47 | 0.5795 |
| 1.3333 | 48 | 0.4643 |
| 1.3611 | 49 | 0.4998 |
| 1.3889 | 50 | 0.4235 |
| 1.4167 | 51 | 0.5118 |
| 1.4444 | 52 | 0.4707 |
| 1.4722 | 53 | 0.4705 |
| 1.5 | 54 | 0.4539 |
| 1.5278 | 55 | 0.5652 |
| 1.5556 | 56 | 0.404 |
| 1.5833 | 57 | 0.5273 |
| 1.6111 | 58 | 0.5888 |
| 1.6389 | 59 | 0.4139 |
| 1.6667 | 60 | 0.4815 |
| 1.6944 | 61 | 0.4656 |
| 1.7222 | 62 | 0.3471 |
| 1.75 | 63 | 0.4345 |
| 1.7778 | 64 | 0.4375 |
| 1.8056 | 65 | 0.3994 |
| 1.8333 | 66 | 0.4184 |
| 1.8611 | 67 | 0.4474 |
| 1.8889 | 68 | 0.3888 |
| 1.9167 | 69 | 0.3873 |
| 1.9444 | 70 | 0.5267 |
| 1.9722 | 71 | 0.3954 |
| 2.0 | 72 | 0.0789 |
| 2.0278 | 73 | 0.429 |
| 2.0556 | 74 | 0.4103 |
| 2.0833 | 75 | 0.3696 |
| 2.1111 | 76 | 0.426 |
| 2.1389 | 77 | 0.3726 |
| 2.1667 | 78 | 0.4097 |
| 2.1944 | 79 | 0.4385 |
| 2.2222 | 80 | 0.3634 |
| 2.25 | 81 | 0.346 |
| 2.2778 | 82 | 0.3483 |
| 2.3056 | 83 | 0.4737 |
| 2.3333 | 84 | 0.4918 |
| 2.3611 | 85 | 0.3644 |
| 2.3889 | 86 | 0.4132 |
| 2.4167 | 87 | 0.422 |
| 2.4444 | 88 | 0.5443 |
| 2.4722 | 89 | 0.4509 |
| 2.5 | 90 | 0.3926 |
| 2.5278 | 91 | 0.3734 |
| 2.5556 | 92 | 0.3753 |
| 2.5833 | 93 | 0.3722 |
| 2.6111 | 94 | 0.4094 |
| 2.6389 | 95 | 0.4425 |
| 2.6667 | 96 | 0.374 |
| 2.6944 | 97 | 0.4313 |
| 2.7222 | 98 | 0.3245 |
| 2.75 | 99 | 0.3582 |
| 2.7778 | 100 | 0.3581 |
| 2.8056 | 101 | 0.3798 |
| 2.8333 | 102 | 0.3791 |
| 2.8611 | 103 | 0.3892 |
| 2.8889 | 104 | 0.3989 |
| 2.9167 | 105 | 0.3393 |
| 2.9444 | 106 | 0.457 |
| 2.9722 | 107 | 0.3486 |
| 3.0 | 108 | 0.1888 |