File size: 28,836 Bytes
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language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:1375067
- loss:MultipleNegativesRankingLoss
base_model: unsloth/all-MiniLM-L6-v2
widget:
- source_sentence: "Modify the inner parameters of the Kepler propagator in order\
\ to place\n the spacecraft in the right Sphere of Influence"
sentences:
- "func (c *Conn) SetDeadline(t time.Time) error {\n\treturn c.p.SetDeadline(t)\n\
}"
- "def _change_soi(self, body):\n \n\n if body == self.central:\n\
\ self.bodies = [self.central]\n self.step = self.central_step\n\
\ self.active = self.central.name\n self.frame = self.central.name\n\
\ else:\n soi = self.SOI[body.name]\n self.bodies\
\ = [body]\n self.step = self.alt_step\n self.active = body.name\n\
\ self.frame = soi.frame"
- "def main(args=None):\n \"\"\"\"\"\"\n parser = _parser()\n\n # Python\
\ 2 will error 'too few arguments' if no subcommand is supplied.\n # No such\
\ error occurs in Python 3, which makes it feasible to check\n # whether a\
\ subcommand was provided (displaying a help message if not).\n # argparse\
\ internals vary significantly over the major versions, so it's\n # much easier\
\ to just override the args passed to it. In this case, print\n # the usage\
\ message if there are no args.\n if args is None and len(sys.argv) <= 1:\n\
\ sys.argv.append('--help')\n\n options = parser.parse_args(args)\n\n\
\ # pass options to subcommand\n options.func(options)\n\n return 0"
- source_sentence: 'Load image from path.
@param path Path to image.
@return Image
@throws java.io.IOException
@throws NullPointerException if {@code path} is null.'
sentences:
- "public function admin_modal_bail( $item_id, $item_title, $field_args ) {\n\n\t\
\t$model_data = $this->build_dfv_field_item_data_recurse_item( $item_id, $item_title,\
\ $field_args );\n\t\t?>\n\t\t\t<script type=\"text/javascript\">\n\t\t\t\twindow.parent.jQuery(\
\ window.parent ).trigger(\n\t\t\t\t\t'dfv:modal:update',\n\t\t\t\t\t<?php echo\
\ wp_json_encode( $model_data, JSON_HEX_TAG ); ?>\n\t\t\t\t);\n\t\t\t</script>\n\
\t\t<?php\n\n\t\tdie();\n\n\t}"
- "private Image loadImage(Resource path) throws IOException {\n\t\tURL url = path.getURL();\n\
\t\tif (url == null) {\n\t\t\tlogger.warn(\"Unable to locate splash screen in\
\ classpath at: \" + path);\n\t\t\treturn null;\n\t\t}\n\t\treturn Toolkit.getDefaultToolkit().createImage(url);\n\
\t}"
- "def generate_wakeword_pieces(self, volume):\n \"\"\"\"\"\"\n while\
\ True:\n target = 1 if random() > 0.5 else 0\n it = self.pos_files_it\
\ if target else self.neg_files_it\n sample_file = next(it)\n \
\ yield self.layer_with(self.normalize_volume_to(load_audio(sample_file),\
\ volume), target)\n yield self.layer_with(np.zeros(int(pr.sample_rate\
\ * (0.5 + 2.0 * random()))), 0)"
- source_sentence: // StartPlugins starts all plugins in the correct order.
sentences:
- "func (co *Coordinator) StartPlugins() {\n\t// Launch routers\n\tfor _, router\
\ := range co.routers {\n\t\tlogrus.Debug(\"Starting \", reflect.TypeOf(router))\n\
\t\tif err := router.Start(); err != nil {\n\t\t\tlogrus.WithError(err).Errorf(\"\
Failed to start router of type '%s'\", reflect.TypeOf(router))\n\t\t}\n\t}\n\n\
\t// Launch producers\n\tco.state = coordinatorStateStartProducers\n\tfor _, producer\
\ := range co.producers {\n\t\tproducer := producer\n\t\tgo tgo.WithRecoverShutdown(func()\
\ {\n\t\t\tlogrus.Debug(\"Starting \", reflect.TypeOf(producer))\n\t\t\tproducer.Produce(co.producerWorker)\n\
\t\t})\n\t}\n\n\t// Set final log target and purge the intermediate buffer\n\t\
if core.StreamRegistry.IsStreamRegistered(core.LogInternalStreamID) {\n\t\t//\
\ The _GOLLUM_ stream has listeners, so use LogConsumer to write to it\n\t\tif\
\ *flagLogColors == \"always\" {\n\t\t\tlogrus.SetFormatter(logger.NewConsoleFormatter())\n\
\t\t}\n\t\tlogrusHookBuffer.SetTargetHook(co.logConsumer)\n\t\tlogrusHookBuffer.Purge()\n\
\n\t} else {\n\t\tlogrusHookBuffer.SetTargetWriter(logger.FallbackLogDevice)\n\
\t\tlogrusHookBuffer.Purge()\n\t}\n\n\t// Launch consumers\n\tco.state = coordinatorStateStartConsumers\n\
\tfor _, consumer := range co.consumers {\n\t\tconsumer := consumer\n\t\tgo tgo.WithRecoverShutdown(func()\
\ {\n\t\t\tlogrus.Debug(\"Starting \", reflect.TypeOf(consumer))\n\t\t\tconsumer.Consume(co.consumerWorker)\n\
\t\t})\n\t}\n}"
- "def __add_symbols(self, cmd):\n \n\n if self.__config.define_symbols:\n\
\ symbols = self.__config.define_symbols\n cmd.append(''.join(\n\
\ [' -D\"%s\"' % def_symbol for def_symbol in symbols]))\n\n \
\ if self.__config.undefine_symbols:\n un_symbols = self.__config.undefine_symbols\n\
\ cmd.append(''.join(\n [' -U\"%s\"' % undef_symbol\
\ for undef_symbol in un_symbols]))\n\n return cmd"
- "protected function addReview()\n {\n if (!$this->isError()) {\n \
\ $id = $this->review->add($this->getSubmitted());\n if (empty($id))\
\ {\n $this->errorAndExit($this->text('Unexpected result'));\n\
\ }\n $this->line($id);\n }\n }"
- source_sentence: Modifies the result of each promise from a scalar value to a object
containing its fieldname
sentences:
- "public void assertUniqueBeans(Set<String> ignoredDuplicateBeanNames) {\n\t\t\
for (BeanohBeanFactoryMethodInterceptor callback : callbacks) {\n\t\t\tMap<String,\
\ List<BeanDefinition>> beanDefinitionMap = callback\n\t\t\t\t\t.getBeanDefinitionMap();\n\
\t\t\tfor (String key : beanDefinitionMap.keySet()) {\n\t\t\t\tif (!ignoredDuplicateBeanNames.contains(key))\
\ {\n\t\t\t\t\tList<BeanDefinition> definitions = beanDefinitionMap\n\t\t\t\t\t\
\t\t.get(key);\n\t\t\t\t\tList<String> resourceDescriptions = new ArrayList<String>();\n\
\t\t\t\t\tfor (BeanDefinition definition : definitions) {\n\t\t\t\t\t\tString\
\ resourceDescription = definition\n\t\t\t\t\t\t\t\t.getResourceDescription();\n\
\t\t\t\t\t\tif (resourceDescription == null) {\n\t\t\t\t\t\t\tresourceDescriptions.add(definition.getBeanClassName());\n\
\t\t\t\t\t\t}else if (!resourceDescription\n\t\t\t\t\t\t\t\t.endsWith(\"-BeanohContext.xml]\"\
)) {\n\t\t\t\t\t\t\tif(!resourceDescriptions.contains(resourceDescription)){\n\
\t\t\t\t\t\t\t\tresourceDescriptions.add(resourceDescription);\n\t\t\t\t\t\t\t\
}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tif (resourceDescriptions.size() > 1)\
\ {\n\t\t\t\t\t\tthrow new DuplicateBeanDefinitionException(\"Bean '\"\n\t\t\t\
\t\t\t\t\t+ key + \"' was defined \"\n\t\t\t\t\t\t\t\t+ resourceDescriptions.size()\
\ + \" times.\\n\"\n\t\t\t\t\t\t\t\t+ \"Either remove duplicate bean definitions\
\ or ignore them with the 'ignoredDuplicateBeanNames' method.\\n\"\n\t\t\t\t\t\
\t\t\t+ \"Configuration locations:\"\n\t\t\t\t\t\t\t\t+ messageUtil.list(resourceDescriptions));\n\
\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}"
- "function wrap(fieldName, promise, args) {\n return promise(args).then((result)\
\ => ({\n [fieldName]: result,\n }));\n}"
- "func Convert_kops_LyftVPCNetworkingSpec_To_v1alpha1_LyftVPCNetworkingSpec(in\
\ *kops.LyftVPCNetworkingSpec, out *LyftVPCNetworkingSpec, s conversion.Scope)\
\ error {\n\treturn autoConvert_kops_LyftVPCNetworkingSpec_To_v1alpha1_LyftVPCNetworkingSpec(in,\
\ out, s)\n}"
- source_sentence: '<p>
User-supplied properties in key-value form.
</p>
@param parameters
User-supplied properties in key-value form.
@return Returns a reference to this object so that method calls can be chained
together.'
sentences:
- "public static function unserializeFromStringRepresentation($string)\n {\n\
\ if (!preg_match('~k:(?P<k>\\d+)/m:(?P<m>\\d+)\\((?P<bitfield>[0-9a-zA-Z+/=]+)\\\
)~', $string, $matches)) {\n throw new InvalidArgumentException('Invalid\
\ string representation');\n }\n $bf = new self((int) $matches['m'],\
\ (int) $matches['k']);\n $bf->bitField = base64_decode($matches['bitfield']);\n\
\ return $bf;\n }"
- "public static function flushEventListeners()\n {\n if (! isset(static::$dispatcher))\
\ {\n return;\n }\n\n $instance = new static;\n\n \
\ foreach ($instance->getObservableEvents() as $event) {\n static::$dispatcher->forget(\"\
eloquent.{$event}: \".static::class);\n }\n\n foreach (array_values($instance->dispatchesEvents)\
\ as $event) {\n static::$dispatcher->forget($event);\n }\n\
\ }"
- "public StorageDescriptor withParameters(java.util.Map<String, String> parameters)\
\ {\n setParameters(parameters);\n return this;\n }"
datasets:
- sentence-transformers/codesearchnet
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on unsloth/all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [unsloth/all-MiniLM-L6-v2](https://huggingface.co/unsloth/all-MiniLM-L6-v2) on the [codesearchnet](https://huggingface.co/datasets/sentence-transformers/codesearchnet) dataset. 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:** [unsloth/all-MiniLM-L6-v2](https://huggingface.co/unsloth/all-MiniLM-L6-v2) <!-- at revision 0f79ca30c044e92859f5852d3a29fb6e976741cd -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [codesearchnet](https://huggingface.co/datasets/sentence-transformers/codesearchnet)
- **Language:** en
<!-- - **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': 'PeftModelForFeatureExtraction'})
(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("sentence_transformers_model_id")
# Run inference
sentences = [
'<p>\nUser-supplied properties in key-value form.\n</p>\n\n@param parameters\nUser-supplied properties in key-value form.\n@return Returns a reference to this object so that method calls can be chained together.',
'public StorageDescriptor withParameters(java.util.Map<String, String> parameters) {\n setParameters(parameters);\n return this;\n }',
"public static function unserializeFromStringRepresentation($string)\n {\n if (!preg_match('~k:(?P<k>\\d+)/m:(?P<m>\\d+)\\((?P<bitfield>[0-9a-zA-Z+/=]+)\\)~', $string, $matches)) {\n throw new InvalidArgumentException('Invalid string representation');\n }\n $bf = new self((int) $matches['m'], (int) $matches['k']);\n $bf->bitField = base64_decode($matches['bitfield']);\n return $bf;\n }",
]
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, 0.6597, -0.0469],
# [ 0.6597, 1.0000, 0.0107],
# [-0.0469, 0.0107, 1.0000]], dtype=torch.float16)
```
<!--
### 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
#### codesearchnet
* Dataset: [codesearchnet](https://huggingface.co/datasets/sentence-transformers/codesearchnet) at [079a958](https://huggingface.co/datasets/sentence-transformers/codesearchnet/tree/079a958b01dc87cf07b66a68414c4b4196d889cc)
* Size: 1,375,067 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: 4 tokens</li><li>mean: 29.95 tokens</li><li>max: 127 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 131.03 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-----------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Computes the new parent id for the node being moved.<br><br>@return int</code> | <code>protected function parentId()<br> {<br> switch ( $this->position )<br> {<br> case 'root':<br> return null;<br><br> case 'child':<br> return $this->target->getKey();<br><br> default:<br> return $this->target->getParentId();<br> }<br> }</code> |
| <code>// SetWinSize overwrites the playlist's window size.</code> | <code>func (p *MediaPlaylist) SetWinSize(winsize uint) error {<br> if winsize > p.capacity {<br> return errors.New("capacity must be greater than winsize or equal")<br> }<br> p.winsize = winsize<br> return nil<br>}</code> |
| <code>Show the sidebar and squish the container to make room for the sidebar.<br>If hideOthers is true, hide other open sidebars.</code> | <code>function() {<br> var options = this.options;<br><br> if (options.hideOthers) {<br> this.secondary.each(function() {<br> var sidebar = $(this);<br><br> if (sidebar.hasClass('is-expanded')) {<br> sidebar.toolkit('offCanvas', 'hide');<br> }<br> });<br> }<br><br> this.fireEvent('showing');<br><br> this.container.addClass('move-' + this.opposite);<br><br> this.element<br> .reveal()<br> .addClass('is-expanded')<br> .aria('expanded', true);<br><br> if (options.stopScroll) {<br> $('body').addClass('no-scroll');<br> }<br><br> this.fireEvent('shown');<br> }</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
- `gradient_accumulation_steps`: 4
- `learning_rate`: 0.0002
- `num_train_epochs`: 2
- `warmup_ratio`: 0.03
- `fp16`: True
- `batch_sampler`: no_duplicates
#### 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`: 4
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 0.0002
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.03
- `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`: 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
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.0186 | 50 | 0.5333 |
| 0.0372 | 100 | 0.3948 |
| 0.0559 | 150 | 0.311 |
| 0.0745 | 200 | 0.2721 |
| 0.0931 | 250 | 0.2809 |
| 0.1117 | 300 | 0.2533 |
| 0.1303 | 350 | 0.2472 |
| 0.1489 | 400 | 0.2378 |
| 0.1676 | 450 | 0.2383 |
| 0.1862 | 500 | 0.2239 |
| 0.2048 | 550 | 0.2236 |
| 0.2234 | 600 | 0.2191 |
| 0.2420 | 650 | 0.2248 |
| 0.2606 | 700 | 0.2176 |
| 0.2793 | 750 | 0.2171 |
| 0.2979 | 800 | 0.2114 |
| 0.3165 | 850 | 0.222 |
| 0.3351 | 900 | 0.2066 |
| 0.3537 | 950 | 0.2059 |
| 0.3723 | 1000 | 0.2053 |
| 0.3910 | 1050 | 0.2011 |
| 0.4096 | 1100 | 0.2024 |
| 0.4282 | 1150 | 0.2006 |
| 0.4468 | 1200 | 0.1976 |
| 0.4654 | 1250 | 0.1968 |
| 0.4840 | 1300 | 0.195 |
| 0.5027 | 1350 | 0.1921 |
| 0.5213 | 1400 | 0.1967 |
| 0.5399 | 1450 | 0.1895 |
| 0.5585 | 1500 | 0.1864 |
| 0.5771 | 1550 | 0.189 |
| 0.5957 | 1600 | 0.1857 |
| 0.6144 | 1650 | 0.1889 |
| 0.6330 | 1700 | 0.1796 |
| 0.6516 | 1750 | 0.1718 |
| 0.6702 | 1800 | 0.1866 |
| 0.6888 | 1850 | 0.1874 |
| 0.7074 | 1900 | 0.178 |
| 0.7261 | 1950 | 0.1763 |
| 0.7447 | 2000 | 0.1734 |
| 0.7633 | 2050 | 0.1823 |
| 0.7819 | 2100 | 0.1796 |
| 0.8005 | 2150 | 0.1737 |
| 0.8191 | 2200 | 0.1796 |
| 0.8378 | 2250 | 0.1794 |
| 0.8564 | 2300 | 0.1703 |
| 0.8750 | 2350 | 0.1746 |
| 0.8936 | 2400 | 0.1864 |
| 0.9122 | 2450 | 0.173 |
| 0.9308 | 2500 | 0.1729 |
| 0.9495 | 2550 | 0.1742 |
| 0.9681 | 2600 | 0.1776 |
| 0.9867 | 2650 | 0.182 |
| 1.0052 | 2700 | 0.1661 |
| 1.0238 | 2750 | 0.1627 |
| 1.0424 | 2800 | 0.158 |
| 1.0611 | 2850 | 0.1585 |
| 1.0797 | 2900 | 0.1555 |
| 1.0983 | 2950 | 0.1566 |
| 1.1169 | 3000 | 0.1511 |
| 1.1355 | 3050 | 0.1557 |
| 1.1541 | 3100 | 0.1589 |
| 1.1728 | 3150 | 0.1545 |
| 1.1914 | 3200 | 0.1567 |
| 1.2100 | 3250 | 0.1561 |
| 1.2286 | 3300 | 0.1515 |
| 1.2472 | 3350 | 0.153 |
| 1.2658 | 3400 | 0.1557 |
| 1.2845 | 3450 | 0.1506 |
| 1.3031 | 3500 | 0.1572 |
| 1.3217 | 3550 | 0.1543 |
| 1.3403 | 3600 | 0.1619 |
| 1.3589 | 3650 | 0.1586 |
| 1.3775 | 3700 | 0.16 |
| 1.3962 | 3750 | 0.1594 |
| 1.4148 | 3800 | 0.1528 |
| 1.4334 | 3850 | 0.1516 |
| 1.4520 | 3900 | 0.1529 |
| 1.4706 | 3950 | 0.149 |
| 1.4892 | 4000 | 0.1572 |
| 1.5079 | 4050 | 0.1505 |
| 1.5265 | 4100 | 0.1552 |
| 1.5451 | 4150 | 0.1488 |
| 1.5637 | 4200 | 0.161 |
| 1.5823 | 4250 | 0.151 |
| 1.6009 | 4300 | 0.1442 |
| 1.6196 | 4350 | 0.1511 |
| 1.6382 | 4400 | 0.1475 |
| 1.6568 | 4450 | 0.1509 |
| 1.6754 | 4500 | 0.1512 |
| 1.6940 | 4550 | 0.1484 |
| 1.7127 | 4600 | 0.1491 |
| 1.7313 | 4650 | 0.143 |
| 1.7499 | 4700 | 0.1479 |
| 1.7685 | 4750 | 0.1459 |
| 1.7871 | 4800 | 0.1434 |
| 1.8057 | 4850 | 0.1475 |
| 1.8244 | 4900 | 0.1485 |
| 1.8430 | 4950 | 0.147 |
| 1.8616 | 5000 | 0.157 |
| 1.8802 | 5050 | 0.1447 |
| 1.8988 | 5100 | 0.1425 |
| 1.9174 | 5150 | 0.1491 |
| 1.9361 | 5200 | 0.1433 |
| 1.9547 | 5250 | 0.1382 |
| 1.9733 | 5300 | 0.1391 |
| 1.9919 | 5350 | 0.1492 |
</details>
### Framework Versions
- Python: 3.12.12
- Sentence Transformers: 5.1.1
- Transformers: 4.57.1
- PyTorch: 2.10.0+cu128
- Accelerate: 1.11.0
- Datasets: 4.3.0
- 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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