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Devy1
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MiniLM-cosqa-128

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:9020
loss:MultipleNegativesRankingLoss
text-embeddings-inference
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MiniLM-cosqa-128 / README.md
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 ---
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-128")
# 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, 0.4828, -0.0626],
# [ 0.4828, 1.0000, -0.0528],
# [-0.0626, -0.0528, 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`: 128
- `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`: 128
- `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 |
|:------:|:----:|:-------------:|
| 0.0141 | 1 | 0.6881 |
| 0.0282 | 2 | 0.4421 |
| 0.0423 | 3 | 0.3636 |
| 0.0563 | 4 | 0.4092 |
| 0.0704 | 5 | 0.4558 |
| 0.0845 | 6 | 0.5227 |
| 0.0986 | 7 | 0.6376 |
| 0.1127 | 8 | 0.4178 |
| 0.1268 | 9 | 0.2803 |
| 0.1408 | 10 | 0.3843 |
| 0.1549 | 11 | 0.3998 |
| 0.1690 | 12 | 0.3264 |
| 0.1831 | 13 | 0.4509 |
| 0.1972 | 14 | 0.4697 |
| 0.2113 | 15 | 0.3188 |
| 0.2254 | 16 | 0.5552 |
| 0.2394 | 17 | 0.3308 |
| 0.2535 | 18 | 0.4426 |
| 0.2676 | 19 | 0.3757 |
| 0.2817 | 20 | 0.2844 |
| 0.2958 | 21 | 0.3652 |
| 0.3099 | 22 | 0.341 |
| 0.3239 | 23 | 0.3956 |
| 0.3380 | 24 | 0.4095 |
| 0.3521 | 25 | 0.3498 |
| 0.3662 | 26 | 0.3957 |
| 0.3803 | 27 | 0.4788 |
| 0.3944 | 28 | 0.4238 |
| 0.4085 | 29 | 0.3866 |
| 0.4225 | 30 | 0.4671 |
| 0.4366 | 31 | 0.358 |
| 0.4507 | 32 | 0.4684 |
| 0.4648 | 33 | 0.4192 |
| 0.4789 | 34 | 0.3826 |
| 0.4930 | 35 | 0.3387 |
| 0.5070 | 36 | 0.4292 |
| 0.5211 | 37 | 0.4378 |
| 0.5352 | 38 | 0.3185 |
| 0.5493 | 39 | 0.3687 |
| 0.5634 | 40 | 0.3171 |
| 0.5775 | 41 | 0.3343 |
| 0.5915 | 42 | 0.4706 |
| 0.6056 | 43 | 0.3747 |
| 0.6197 | 44 | 0.3272 |
| 0.6338 | 45 | 0.4118 |
| 0.6479 | 46 | 0.4688 |
| 0.6620 | 47 | 0.3684 |
| 0.6761 | 48 | 0.3609 |
| 0.6901 | 49 | 0.3521 |
| 0.7042 | 50 | 0.3533 |
| 0.7183 | 51 | 0.3788 |
| 0.7324 | 52 | 0.3182 |
| 0.7465 | 53 | 0.5793 |
| 0.7606 | 54 | 0.2803 |
| 0.7746 | 55 | 0.2695 |
| 0.7887 | 56 | 0.2853 |
| 0.8028 | 57 | 0.3116 |
| 0.8169 | 58 | 0.3542 |
| 0.8310 | 59 | 0.3445 |
| 0.8451 | 60 | 0.2799 |
| 0.8592 | 61 | 0.3178 |
| 0.8732 | 62 | 0.4737 |
| 0.8873 | 63 | 0.2121 |
| 0.9014 | 64 | 0.2585 |
| 0.9155 | 65 | 0.3238 |
| 0.9296 | 66 | 0.3203 |
| 0.9437 | 67 | 0.4475 |
| 0.9577 | 68 | 0.3722 |
| 0.9718 | 69 | 0.4047 |
| 0.9859 | 70 | 0.3056 |
| 1.0 | 71 | 0.316 |
| 1.0141 | 72 | 0.2711 |
| 1.0282 | 73 | 0.3488 |
| 1.0423 | 74 | 0.2413 |
| 1.0563 | 75 | 0.2434 |
| 1.0704 | 76 | 0.2602 |
| 1.0845 | 77 | 0.3006 |
| 1.0986 | 78 | 0.237 |
| 1.1127 | 79 | 0.2614 |
| 1.1268 | 80 | 0.2456 |
| 1.1408 | 81 | 0.2305 |
| 1.1549 | 82 | 0.2774 |
| 1.1690 | 83 | 0.3028 |
| 1.1831 | 84 | 0.2037 |
| 1.1972 | 85 | 0.2905 |
| 1.2113 | 86 | 0.2048 |
| 1.2254 | 87 | 0.2459 |
| 1.2394 | 88 | 0.2291 |
| 1.2535 | 89 | 0.2319 |
| 1.2676 | 90 | 0.2755 |
| 1.2817 | 91 | 0.3138 |
| 1.2958 | 92 | 0.3555 |
| 1.3099 | 93 | 0.2908 |
| 1.3239 | 94 | 0.2602 |
| 1.3380 | 95 | 0.2615 |
| 1.3521 | 96 | 0.2041 |
| 1.3662 | 97 | 0.2629 |
| 1.3803 | 98 | 0.2508 |
| 1.3944 | 99 | 0.248 |
| 1.4085 | 100 | 0.2601 |
| 1.4225 | 101 | 0.3114 |
| 1.4366 | 102 | 0.3201 |
| 1.4507 | 103 | 0.2574 |
| 1.4648 | 104 | 0.2371 |
| 1.4789 | 105 | 0.2041 |
| 1.4930 | 106 | 0.2454 |
| 1.5070 | 107 | 0.3303 |
| 1.5211 | 108 | 0.29 |
| 1.5352 | 109 | 0.3327 |
| 1.5493 | 110 | 0.2741 |
| 1.5634 | 111 | 0.258 |
| 1.5775 | 112 | 0.3228 |
| 1.5915 | 113 | 0.2989 |
| 1.6056 | 114 | 0.2769 |
| 1.6197 | 115 | 0.3744 |
| 1.6338 | 116 | 0.3053 |
| 1.6479 | 117 | 0.1675 |
| 1.6620 | 118 | 0.2337 |
| 1.6761 | 119 | 0.2505 |
| 1.6901 | 120 | 0.2304 |
| 1.7042 | 121 | 0.2369 |
| 1.7183 | 122 | 0.1978 |
| 1.7324 | 123 | 0.1929 |
| 1.7465 | 124 | 0.2212 |
| 1.7606 | 125 | 0.2175 |
| 1.7746 | 126 | 0.1839 |
| 1.7887 | 127 | 0.3059 |
| 1.8028 | 128 | 0.1996 |
| 1.8169 | 129 | 0.3 |
| 1.8310 | 130 | 0.3051 |
| 1.8451 | 131 | 0.2272 |
| 1.8592 | 132 | 0.2503 |
| 1.8732 | 133 | 0.3077 |
| 1.8873 | 134 | 0.1847 |
| 1.9014 | 135 | 0.2437 |
| 1.9155 | 136 | 0.2333 |
| 1.9296 | 137 | 0.2111 |
| 1.9437 | 138 | 0.162 |
| 1.9577 | 139 | 0.4412 |
| 1.9718 | 140 | 0.1282 |
| 1.9859 | 141 | 0.2651 |
| 2.0 | 142 | 0.1055 |
| 2.0141 | 143 | 0.2316 |
| 2.0282 | 144 | 0.243 |
| 2.0423 | 145 | 0.1892 |
| 2.0563 | 146 | 0.19 |
| 2.0704 | 147 | 0.172 |
| 2.0845 | 148 | 0.185 |
| 2.0986 | 149 | 0.2481 |
| 2.1127 | 150 | 0.2651 |
| 2.1268 | 151 | 0.2511 |
| 2.1408 | 152 | 0.1761 |
| 2.1549 | 153 | 0.2215 |
| 2.1690 | 154 | 0.2275 |
| 2.1831 | 155 | 0.2621 |
| 2.1972 | 156 | 0.2255 |
| 2.2113 | 157 | 0.201 |
| 2.2254 | 158 | 0.1372 |
| 2.2394 | 159 | 0.1941 |
| 2.2535 | 160 | 0.2225 |
| 2.2676 | 161 | 0.1713 |
| 2.2817 | 162 | 0.1045 |
| 2.2958 | 163 | 0.2273 |
| 2.3099 | 164 | 0.2474 |
| 2.3239 | 165 | 0.312 |
| 2.3380 | 166 | 0.2274 |
| 2.3521 | 167 | 0.1991 |
| 2.3662 | 168 | 0.1511 |
| 2.3803 | 169 | 0.2248 |
| 2.3944 | 170 | 0.2025 |
| 2.4085 | 171 | 0.258 |
| 2.4225 | 172 | 0.2163 |
| 2.4366 | 173 | 0.4012 |
| 2.4507 | 174 | 0.2397 |
| 2.4648 | 175 | 0.1978 |
| 2.4789 | 176 | 0.2071 |
| 2.4930 | 177 | 0.147 |
| 2.5070 | 178 | 0.2424 |
| 2.5211 | 179 | 0.1345 |
| 2.5352 | 180 | 0.2506 |
| 2.5493 | 181 | 0.1275 |
| 2.5634 | 182 | 0.3284 |
| 2.5775 | 183 | 0.2063 |
| 2.5915 | 184 | 0.1483 |
| 2.6056 | 185 | 0.2051 |
| 2.6197 | 186 | 0.2439 |
| 2.6338 | 187 | 0.252 |
| 2.6479 | 188 | 0.2126 |
| 2.6620 | 189 | 0.2156 |
| 2.6761 | 190 | 0.153 |
| 2.6901 | 191 | 0.2481 |
| 2.7042 | 192 | 0.2481 |
| 2.7183 | 193 | 0.1539 |
| 2.7324 | 194 | 0.1224 |
| 2.7465 | 195 | 0.1924 |
| 2.7606 | 196 | 0.196 |
| 2.7746 | 197 | 0.2172 |
| 2.7887 | 198 | 0.1999 |
| 2.8028 | 199 | 0.1932 |
| 2.8169 | 200 | 0.1758 |
| 2.8310 | 201 | 0.2173 |
| 2.8451 | 202 | 0.1792 |
| 2.8592 | 203 | 0.2228 |
| 2.8732 | 204 | 0.2013 |
| 2.8873 | 205 | 0.2197 |
| 2.9014 | 206 | 0.1942 |
| 2.9155 | 207 | 0.1798 |
| 2.9296 | 208 | 0.2064 |
| 2.9437 | 209 | 0.2901 |
| 2.9577 | 210 | 0.202 |
| 2.9718 | 211 | 0.1809 |
| 2.9859 | 212 | 0.176 |
| 3.0 | 213 | 0.1733 |
</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}
}
```
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