Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "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
+}

README.md

@@ -0,0 +1,834 @@
+---
+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-64")
+# 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,  0.4847, -0.0572],
+#         [ 0.4847,  1.0000, -0.0541],
+#         [-0.0572, -0.0541,  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: 9,020 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`: 64
+- `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`: 64
+- `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
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.0071 | 1    | 0.4603        |
+| 0.0142 | 2    | 0.3179        |
+| 0.0213 | 3    | 0.1802        |
+| 0.0284 | 4    | 0.2268        |
+| 0.0355 | 5    | 0.2288        |
+| 0.0426 | 6    | 0.1769        |
+| 0.0496 | 7    | 0.1555        |
+| 0.0567 | 8    | 0.2626        |
+| 0.0638 | 9    | 0.3319        |
+| 0.0709 | 10   | 0.28          |
+| 0.0780 | 11   | 0.3356        |
+| 0.0851 | 12   | 0.3241        |
+| 0.0922 | 13   | 0.2933        |
+| 0.0993 | 14   | 0.3929        |
+| 0.1064 | 15   | 0.1861        |
+| 0.1135 | 16   | 0.1983        |
+| 0.1206 | 17   | 0.1605        |
+| 0.1277 | 18   | 0.0918        |
+| 0.1348 | 19   | 0.2831        |
+| 0.1418 | 20   | 0.1709        |
+| 0.1489 | 21   | 0.1984        |
+| 0.1560 | 22   | 0.2657        |
+| 0.1631 | 23   | 0.1619        |
+| 0.1702 | 24   | 0.1728        |
+| 0.1773 | 25   | 0.1791        |
+| 0.1844 | 26   | 0.2429        |
+| 0.1915 | 27   | 0.2743        |
+| 0.1986 | 28   | 0.2813        |
+| 0.2057 | 29   | 0.2192        |
+| 0.2128 | 30   | 0.166         |
+| 0.2199 | 31   | 0.2557        |
+| 0.2270 | 32   | 0.3556        |
+| 0.2340 | 33   | 0.2238        |
+| 0.2411 | 34   | 0.2552        |
+| 0.2482 | 35   | 0.2266        |
+| 0.2553 | 36   | 0.4347        |
+| 0.2624 | 37   | 0.2803        |
+| 0.2695 | 38   | 0.1219        |
+| 0.2766 | 39   | 0.1989        |
+| 0.2837 | 40   | 0.2364        |
+| 0.2908 | 41   | 0.2237        |
+| 0.2979 | 42   | 0.1567        |
+| 0.3050 | 43   | 0.2509        |
+| 0.3121 | 44   | 0.16          |
+| 0.3191 | 45   | 0.2148        |
+| 0.3262 | 46   | 0.1953        |
+| 0.3333 | 47   | 0.2447        |
+| 0.3404 | 48   | 0.2001        |
+| 0.3475 | 49   | 0.283         |
+| 0.3546 | 50   | 0.1505        |
+| 0.3617 | 51   | 0.2825        |
+| 0.3688 | 52   | 0.2137        |
+| 0.3759 | 53   | 0.1376        |
+| 0.3830 | 54   | 0.3898        |
+| 0.3901 | 55   | 0.1873        |
+| 0.3972 | 56   | 0.2226        |
+| 0.4043 | 57   | 0.3129        |
+| 0.4113 | 58   | 0.2127        |
+| 0.4184 | 59   | 0.3474        |
+| 0.4255 | 60   | 0.0971        |
+| 0.4326 | 61   | 0.1728        |
+| 0.4397 | 62   | 0.2851        |
+| 0.4468 | 63   | 0.2608        |
+| 0.4539 | 64   | 0.3269        |
+| 0.4610 | 65   | 0.4905        |
+| 0.4681 | 66   | 0.1886        |
+| 0.4752 | 67   | 0.1465        |
+| 0.4823 | 68   | 0.2342        |
+| 0.4894 | 69   | 0.1915        |
+| 0.4965 | 70   | 0.2291        |
+| 0.5035 | 71   | 0.3232        |
+| 0.5106 | 72   | 0.1633        |
+| 0.5177 | 73   | 0.2039        |
+| 0.5248 | 74   | 0.2441        |
+| 0.5319 | 75   | 0.2336        |
+| 0.5390 | 76   | 0.139         |
+| 0.5461 | 77   | 0.4471        |
+| 0.5532 | 78   | 0.1989        |
+| 0.5603 | 79   | 0.2112        |
+| 0.5674 | 80   | 0.1862        |
+| 0.5745 | 81   | 0.2353        |
+| 0.5816 | 82   | 0.2326        |
+| 0.5887 | 83   | 0.3223        |
+| 0.5957 | 84   | 0.2055        |
+| 0.6028 | 85   | 0.2968        |
+| 0.6099 | 86   | 0.2531        |
+| 0.6170 | 87   | 0.2401        |
+| 0.6241 | 88   | 0.1632        |
+| 0.6312 | 89   | 0.4203        |
+| 0.6383 | 90   | 0.1959        |
+| 0.6454 | 91   | 0.2309        |
+| 0.6525 | 92   | 0.3729        |
+| 0.6596 | 93   | 0.2488        |
+| 0.6667 | 94   | 0.1698        |
+| 0.6738 | 95   | 0.267         |
+| 0.6809 | 96   | 0.1658        |
+| 0.6879 | 97   | 0.2158        |
+| 0.6950 | 98   | 0.1665        |
+| 0.7021 | 99   | 0.1897        |
+| 0.7092 | 100  | 0.2159        |
+| 0.7163 | 101  | 0.1932        |
+| 0.7234 | 102  | 0.2236        |
+| 0.7305 | 103  | 0.1287        |
+| 0.7376 | 104  | 0.1917        |
+| 0.7447 | 105  | 0.4039        |
+| 0.7518 | 106  | 0.388         |
+| 0.7589 | 107  | 0.1267        |
+| 0.7660 | 108  | 0.1851        |
+| 0.7730 | 109  | 0.1916        |
+| 0.7801 | 110  | 0.1893        |
+| 0.7872 | 111  | 0.1702        |
+| 0.7943 | 112  | 0.1552        |
+| 0.8014 | 113  | 0.1529        |
+| 0.8085 | 114  | 0.1634        |
+| 0.8156 | 115  | 0.2136        |
+| 0.8227 | 116  | 0.1719        |
+| 0.8298 | 117  | 0.2529        |
+| 0.8369 | 118  | 0.2329        |
+| 0.8440 | 119  | 0.2483        |
+| 0.8511 | 120  | 0.132         |
+| 0.8582 | 121  | 0.182         |
+| 0.8652 | 122  | 0.127         |
+| 0.8723 | 123  | 0.3685        |
+| 0.8794 | 124  | 0.4202        |
+| 0.8865 | 125  | 0.2173        |
+| 0.8936 | 126  | 0.0657        |
+| 0.9007 | 127  | 0.0838        |
+| 0.9078 | 128  | 0.1592        |
+| 0.9149 | 129  | 0.2506        |
+| 0.9220 | 130  | 0.1624        |
+| 0.9291 | 131  | 0.1511        |
+| 0.9362 | 132  | 0.138         |
+| 0.9433 | 133  | 0.2187        |
+| 0.9504 | 134  | 0.2891        |
+| 0.9574 | 135  | 0.158         |
+| 0.9645 | 136  | 0.2595        |
+| 0.9716 | 137  | 0.2911        |
+| 0.9787 | 138  | 0.2141        |
+| 0.9858 | 139  | 0.1723        |
+| 0.9929 | 140  | 0.1847        |
+| 1.0    | 141  | 0.2606        |
+| 1.0071 | 142  | 0.1283        |
+| 1.0142 | 143  | 0.1626        |
+| 1.0213 | 144  | 0.2121        |
+| 1.0284 | 145  | 0.142         |
+| 1.0355 | 146  | 0.1335        |
+| 1.0426 | 147  | 0.1084        |
+| 1.0496 | 148  | 0.15          |
+| 1.0567 | 149  | 0.1459        |
+| 1.0638 | 150  | 0.0674        |
+| 1.0709 | 151  | 0.1393        |
+| 1.0780 | 152  | 0.1582        |
+| 1.0851 | 153  | 0.1295        |
+| 1.0922 | 154  | 0.1402        |
+| 1.0993 | 155  | 0.2266        |
+| 1.1064 | 156  | 0.1025        |
+| 1.1135 | 157  | 0.1616        |
+| 1.1206 | 158  | 0.1795        |
+| 1.1277 | 159  | 0.1583        |
+| 1.1348 | 160  | 0.1624        |
+| 1.1418 | 161  | 0.1068        |
+| 1.1489 | 162  | 0.1301        |
+| 1.1560 | 163  | 0.1792        |
+| 1.1631 | 164  | 0.1656        |
+| 1.1702 | 165  | 0.1666        |
+| 1.1773 | 166  | 0.1031        |
+| 1.1844 | 167  | 0.1092        |
+| 1.1915 | 168  | 0.1668        |
+| 1.1986 | 169  | 0.1218        |
+| 1.2057 | 170  | 0.146         |
+| 1.2128 | 171  | 0.1041        |
+| 1.2199 | 172  | 0.2275        |
+| 1.2270 | 173  | 0.1017        |
+| 1.2340 | 174  | 0.1025        |
+| 1.2411 | 175  | 0.1385        |
+| 1.2482 | 176  | 0.1024        |
+| 1.2553 | 177  | 0.1073        |
+| 1.2624 | 178  | 0.0802        |
+| 1.2695 | 179  | 0.1985        |
+| 1.2766 | 180  | 0.1918        |
+| 1.2837 | 181  | 0.092         |
+| 1.2908 | 182  | 0.1591        |
+| 1.2979 | 183  | 0.2512        |
+| 1.3050 | 184  | 0.2213        |
+| 1.3121 | 185  | 0.129         |
+| 1.3191 | 186  | 0.0759        |
+| 1.3262 | 187  | 0.243         |
+| 1.3333 | 188  | 0.1759        |
+| 1.3404 | 189  | 0.126         |
+| 1.3475 | 190  | 0.1105        |
+| 1.3546 | 191  | 0.1789        |
+| 1.3617 | 192  | 0.1841        |
+| 1.3688 | 193  | 0.1074        |
+| 1.3759 | 194  | 0.1293        |
+| 1.3830 | 195  | 0.1228        |
+| 1.3901 | 196  | 0.1574        |
+| 1.3972 | 197  | 0.1073        |
+| 1.4043 | 198  | 0.1305        |
+| 1.4113 | 199  | 0.1911        |
+| 1.4184 | 200  | 0.1088        |
+| 1.4255 | 201  | 0.111         |
+| 1.4326 | 202  | 0.1639        |
+| 1.4397 | 203  | 0.0944        |
+| 1.4468 | 204  | 0.2008        |
+| 1.4539 | 205  | 0.136         |
+| 1.4610 | 206  | 0.1981        |
+| 1.4681 | 207  | 0.0848        |
+| 1.4752 | 208  | 0.0771        |
+| 1.4823 | 209  | 0.0933        |
+| 1.4894 | 210  | 0.1794        |
+| 1.4965 | 211  | 0.1533        |
+| 1.5035 | 212  | 0.1841        |
+| 1.5106 | 213  | 0.1724        |
+| 1.5177 | 214  | 0.1205        |
+| 1.5248 | 215  | 0.1118        |
+| 1.5319 | 216  | 0.16          |
+| 1.5390 | 217  | 0.2911        |
+| 1.5461 | 218  | 0.1678        |
+| 1.5532 | 219  | 0.1032        |
+| 1.5603 | 220  | 0.1438        |
+| 1.5674 | 221  | 0.1581        |
+| 1.5745 | 222  | 0.1143        |
+| 1.5816 | 223  | 0.1782        |
+| 1.5887 | 224  | 0.2768        |
+| 1.5957 | 225  | 0.1127        |
+| 1.6028 | 226  | 0.1719        |
+| 1.6099 | 227  | 0.2252        |
+| 1.6170 | 228  | 0.2182        |
+| 1.6241 | 229  | 0.287         |
+| 1.6312 | 230  | 0.1314        |
+| 1.6383 | 231  | 0.1951        |
+| 1.6454 | 232  | 0.13          |
+| 1.6525 | 233  | 0.0677        |
+| 1.6596 | 234  | 0.1188        |
+| 1.6667 | 235  | 0.1214        |
+| 1.6738 | 236  | 0.1219        |
+| 1.6809 | 237  | 0.1646        |
+| 1.6879 | 238  | 0.1079        |
+| 1.6950 | 239  | 0.1624        |
+| 1.7021 | 240  | 0.0994        |
+| 1.7092 | 241  | 0.194         |
+| 1.7163 | 242  | 0.1104        |
+| 1.7234 | 243  | 0.1223        |
+| 1.7305 | 244  | 0.0918        |
+| 1.7376 | 245  | 0.0835        |
+| 1.7447 | 246  | 0.0994        |
+| 1.7518 | 247  | 0.1375        |
+| 1.7589 | 248  | 0.1004        |
+| 1.7660 | 249  | 0.1164        |
+| 1.7730 | 250  | 0.1151        |
+| 1.7801 | 251  | 0.0868        |
+| 1.7872 | 252  | 0.2498        |
+| 1.7943 | 253  | 0.0741        |
+| 1.8014 | 254  | 0.1417        |
+| 1.8085 | 255  | 0.0514        |
+| 1.8156 | 256  | 0.2346        |
+| 1.8227 | 257  | 0.2383        |
+| 1.8298 | 258  | 0.1432        |
+| 1.8369 | 259  | 0.1563        |
+| 1.8440 | 260  | 0.1267        |
+| 1.8511 | 261  | 0.1331        |
+| 1.8582 | 262  | 0.1904        |
+| 1.8652 | 263  | 0.0912        |
+| 1.8723 | 264  | 0.214         |
+| 1.8794 | 265  | 0.1846        |
+| 1.8865 | 266  | 0.1378        |
+| 1.8936 | 267  | 0.1012        |
+| 1.9007 | 268  | 0.1468        |
+| 1.9078 | 269  | 0.109         |
+| 1.9149 | 270  | 0.1136        |
+| 1.9220 | 271  | 0.1734        |
+| 1.9291 | 272  | 0.0785        |
+| 1.9362 | 273  | 0.0388        |
+| 1.9433 | 274  | 0.1138        |
+| 1.9504 | 275  | 0.0806        |
+| 1.9574 | 276  | 0.2819        |
+| 1.9645 | 277  | 0.1719        |
+| 1.9716 | 278  | 0.0479        |
+| 1.9787 | 279  | 0.1038        |
+| 1.9858 | 280  | 0.1401        |
+| 1.9929 | 281  | 0.1961        |
+| 2.0    | 282  | 0.1072        |
+| 2.0071 | 283  | 0.1005        |
+| 2.0142 | 284  | 0.147         |
+| 2.0213 | 285  | 0.1011        |
+| 2.0284 | 286  | 0.1304        |
+| 2.0355 | 287  | 0.073         |
+| 2.0426 | 288  | 0.0952        |
+| 2.0496 | 289  | 0.0956        |
+| 2.0567 | 290  | 0.1083        |
+| 2.0638 | 291  | 0.1101        |
+| 2.0709 | 292  | 0.0534        |
+| 2.0780 | 293  | 0.0837        |
+| 2.0851 | 294  | 0.0966        |
+| 2.0922 | 295  | 0.195         |
+| 2.0993 | 296  | 0.0608        |
+| 2.1064 | 297  | 0.0999        |
+| 2.1135 | 298  | 0.1588        |
+| 2.1206 | 299  | 0.1283        |
+| 2.1277 | 300  | 0.0962        |
+| 2.1348 | 301  | 0.0872        |
+| 2.1418 | 302  | 0.0793        |
+| 2.1489 | 303  | 0.1209        |
+| 2.1560 | 304  | 0.1346        |
+| 2.1631 | 305  | 0.131         |
+| 2.1702 | 306  | 0.1081        |
+| 2.1773 | 307  | 0.1109        |
+| 2.1844 | 308  | 0.197         |
+| 2.1915 | 309  | 0.108         |
+| 2.1986 | 310  | 0.1715        |
+| 2.2057 | 311  | 0.0654        |
+| 2.2128 | 312  | 0.1374        |
+| 2.2199 | 313  | 0.0929        |
+| 2.2270 | 314  | 0.033         |
+| 2.2340 | 315  | 0.0903        |
+| 2.2411 | 316  | 0.1417        |
+| 2.2482 | 317  | 0.134         |
+| 2.2553 | 318  | 0.041         |
+| 2.2624 | 319  | 0.0947        |
+| 2.2695 | 320  | 0.0655        |
+| 2.2766 | 321  | 0.0525        |
+| 2.2837 | 322  | 0.0547        |
+| 2.2908 | 323  | 0.1342        |
+| 2.2979 | 324  | 0.1088        |
+| 2.3050 | 325  | 0.162         |
+| 2.3121 | 326  | 0.0962        |
+| 2.3191 | 327  | 0.154         |
+| 2.3262 | 328  | 0.0935        |
+| 2.3333 | 329  | 0.1186        |
+| 2.3404 | 330  | 0.1192        |
+| 2.3475 | 331  | 0.1075        |
+| 2.3546 | 332  | 0.12          |
+| 2.3617 | 333  | 0.0679        |
+| 2.3688 | 334  | 0.1087        |
+| 2.3759 | 335  | 0.1493        |
+| 2.3830 | 336  | 0.085         |
+| 2.3901 | 337  | 0.1784        |
+| 2.3972 | 338  | 0.0567        |
+| 2.4043 | 339  | 0.1842        |
+| 2.4113 | 340  | 0.183         |
+| 2.4184 | 341  | 0.1108        |
+| 2.4255 | 342  | 0.1405        |
+| 2.4326 | 343  | 0.2477        |
+| 2.4397 | 344  | 0.2376        |
+| 2.4468 | 345  | 0.1469        |
+| 2.4539 | 346  | 0.1048        |
+| 2.4610 | 347  | 0.1153        |
+| 2.4681 | 348  | 0.1167        |
+| 2.4752 | 349  | 0.1605        |
+| 2.4823 | 350  | 0.1479        |
+| 2.4894 | 351  | 0.0684        |
+| 2.4965 | 352  | 0.0515        |
+| 2.5035 | 353  | 0.1035        |
+| 2.5106 | 354  | 0.1488        |
+| 2.5177 | 355  | 0.0274        |
+| 2.5248 | 356  | 0.0706        |
+| 2.5319 | 357  | 0.1541        |
+| 2.5390 | 358  | 0.1331        |
+| 2.5461 | 359  | 0.0911        |
+| 2.5532 | 360  | 0.0606        |
+| 2.5603 | 361  | 0.1612        |
+| 2.5674 | 362  | 0.2752        |
+| 2.5745 | 363  | 0.1436        |
+| 2.5816 | 364  | 0.1257        |
+| 2.5887 | 365  | 0.1174        |
+| 2.5957 | 366  | 0.0415        |
+| 2.6028 | 367  | 0.0918        |
+| 2.6099 | 368  | 0.0899        |
+| 2.6170 | 369  | 0.1136        |
+| 2.6241 | 370  | 0.1337        |
+| 2.6312 | 371  | 0.1948        |
+| 2.6383 | 372  | 0.1482        |
+| 2.6454 | 373  | 0.1209        |
+| 2.6525 | 374  | 0.1082        |
+| 2.6596 | 375  | 0.1948        |
+| 2.6667 | 376  | 0.1029        |
+| 2.6738 | 377  | 0.0783        |
+| 2.6809 | 378  | 0.0844        |
+| 2.6879 | 379  | 0.1045        |
+| 2.6950 | 380  | 0.0982        |
+| 2.7021 | 381  | 0.075         |
+| 2.7092 | 382  | 0.15          |
+| 2.7163 | 383  | 0.1155        |
+| 2.7234 | 384  | 0.1334        |
+| 2.7305 | 385  | 0.0767        |
+| 2.7376 | 386  | 0.0476        |
+| 2.7447 | 387  | 0.068         |
+| 2.7518 | 388  | 0.0967        |
+| 2.7589 | 389  | 0.0953        |
+| 2.7660 | 390  | 0.1307        |
+| 2.7730 | 391  | 0.0923        |
+| 2.7801 | 392  | 0.1159        |
+| 2.7872 | 393  | 0.0769        |
+| 2.7943 | 394  | 0.0993        |
+| 2.8014 | 395  | 0.1018        |
+| 2.8085 | 396  | 0.0783        |
+| 2.8156 | 397  | 0.0792        |
+| 2.8227 | 398  | 0.0914        |
+| 2.8298 | 399  | 0.0821        |
+| 2.8369 | 400  | 0.0947        |
+| 2.8440 | 401  | 0.0622        |
+| 2.8511 | 402  | 0.1858        |
+| 2.8582 | 403  | 0.1977        |
+| 2.8652 | 404  | 0.0398        |
+| 2.8723 | 405  | 0.0784        |
+| 2.8794 | 406  | 0.1622        |
+| 2.8865 | 407  | 0.1213        |
+| 2.8936 | 408  | 0.1867        |
+| 2.9007 | 409  | 0.1257        |
+| 2.9078 | 410  | 0.1366        |
+| 2.9149 | 411  | 0.0983        |
+| 2.9220 | 412  | 0.0967        |
+| 2.9291 | 413  | 0.0398        |
+| 2.9362 | 414  | 0.1582        |
+| 2.9433 | 415  | 0.123         |
+| 2.9504 | 416  | 0.1768        |
+| 2.9574 | 417  | 0.131         |
+| 2.9645 | 418  | 0.0731        |
+| 2.9716 | 419  | 0.074         |
+| 2.9787 | 420  | 0.1176        |
+| 2.9858 | 421  | 0.0984        |
+| 2.9929 | 422  | 0.0834        |
+| 3.0    | 423  | 0.1985        |
+
+</details>
+
+### 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}
+}
+```
+
+<!--
+## 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.*
+-->

config.json

@@ -0,0 +1,25 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.56.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "sentence_transformers": "5.1.1",
+    "transformers": "4.56.2",
+    "pytorch": "2.8.0+cu128"
+  },
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a06c4761de826a9dbb4dcfdf49cfe05925a5ffeed26ea0d1c68cb6f6d7656e7a
+size 90864192

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 256,
+    "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,65 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 256,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}