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CodeSearch-ModernBERT-Owl-3.0-Plus

Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
feature-extraction
Generated from Trainer
dataset_size:7059200
loss:MultipleNegativesRankingLoss
text-embeddings-inference
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CodeSearch-ModernBERT-Owl-3.0-Plus / README.md
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 ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:7059200
- loss:MultipleNegativesRankingLoss
base_model: Shuu12121/CodeModernBERT-Owl-3.0
widget:
- source_sentence: 'The maximum value of the slider. (default 0) <P>
@return Returns the value of the attribute, or 0, if it hasn''t been set by the
JSF file.'
sentences:
- "@Override\n public UpdateSmsChannelResult updateSmsChannel(UpdateSmsChannelRequest\
\ request) {\n request = beforeClientExecution(request);\n return\
\ executeUpdateSmsChannel(request);\n }"
- "async function isValidOrigin(origin, sourceOrigin) {\n // This will fetch\
\ the caches from https://cdn.ampproject.org/caches.json the first time it's\n\
\ // called. Subsequent calls will receive a cached version.\n const officialCacheList\
\ = await caches.list();\n // Calculate the cache specific origin\n const\
\ cacheSubdomain = `https://${await createCacheSubdomain(sourceOrigin)}.`;\n \
\ // Check all caches listed on ampproject.org\n for (const cache of officialCacheList)\
\ {\n const cachedOrigin = cacheSubdomain + cache.cacheDomain;\n if\
\ (origin === cachedOrigin) {\n return true;\n }\n }\n return\
\ false;\n }"
- "public java.lang.Object getMin() {\n\t\treturn (java.lang.Object) getStateHelper().eval(PropertyKeys.min,\
\ 0);\n\t}"
- source_sentence: 'The Method from the Date.getMinutes is deprecated. This is a helper-Method.
@param date
The Date-object to get the minutes.
@return The minutes from the Date-object.'
sentences:
- "public static int getMinutes(final Date date)\n\t{\n\t\tfinal Calendar calendar\
\ = Calendar.getInstance();\n\t\tcalendar.setTime(date);\n\t\treturn calendar.get(Calendar.MINUTE);\n\
\t}"
- "func (opts BeeOptions) Bind(name string, dst interface{}) error {\n\tv := opts.Value(name)\n\
\tif v == nil {\n\t\treturn errors.New(\"Option with name \" + name + \" not found\"\
)\n\t}\n\n\treturn ConvertValue(v, dst)\n}"
- "public function createFor(Customer $customer, array $options = [], array $filters\
\ = [])\n {\n $this->parentId = $customer->id;\n\n return parent::rest_create($options,\
\ $filters);\n }"
- source_sentence: "Return a list of all dates from 11/12/2015 to the present.\n\n\
\ Args:\n boo: if true, list contains Numbers (20151230); if false, list contains\
\ Strings (\"2015-12-30\")\n Returns:\n list of either Numbers or Strings"
sentences:
- "def all_days(boo):\n \n earliest = datetime.strptime(('2015-11-12').replace('-',\
\ ' '), '%Y %m %d')\n latest = datetime.strptime(datetime.today().date().isoformat().replace('-',\
\ ' '), '%Y %m %d')\n num_days = (latest - earliest).days + 1\n all_days = [latest\
\ - timedelta(days=x) for x in range(num_days)]\n all_days.reverse()\n\n output\
\ = []\n\n if boo:\n # Return as Integer, yyyymmdd\n for d in all_days:\n\
\ output.append(int(str(d).replace('-', '')[:8]))\n else:\n # Return\
\ as String, yyyy-mm-dd\n for d in all_days:\n output.append(str(d)[:10])\n\
\ return output"
- "public void setColSize3(Integer newColSize3) {\n\t\tInteger oldColSize3 = colSize3;\n\
\t\tcolSize3 = newColSize3;\n\t\tif (eNotificationRequired())\n\t\t\teNotify(new\
\ ENotificationImpl(this, Notification.SET, AfplibPackage.COLOR_SPECIFICATION__COL_SIZE3,\
\ oldColSize3, colSize3));\n\t}"
- "public function deleteCompanyBusinessUnitStoreAddress(CompanyBusinessUnitStoreAddressTransfer\
\ $companyBusinessUnitStoreAddressTransfer): void\n {\n $this->getFactory()\n\
\ ->createFosCompanyBusinessUnitStoreAddressQuery()\n ->findOneByIdCompanyBusinessUnitStoreAddress($companyBusinessUnitStoreAddressTransfer->getIdCompanyBusinessUnitStoreAddress())\n\
\ ->delete();\n }"
- source_sentence: 'Returns array of basket oxarticle objects
@return array'
sentences:
- "public function visit(NodeVisitorInterface $visitor)\n {\n foreach\
\ ($this->children as $child)\n {\n $child->visit($visitor);\n\
\ }\n }"
- "func GetColDefaultValue(ctx sessionctx.Context, col *model.ColumnInfo) (types.Datum,\
\ error) {\n\treturn getColDefaultValue(ctx, col, col.GetDefaultValue())\n}"
- "public function getBasketArticles()\n {\n $aBasketArticles = [];\n\
\ /** @var \\oxBasketItem $oBasketItem */\n foreach ($this->_aBasketContents\
\ as $sItemKey => $oBasketItem) {\n try {\n $oProduct\
\ = $oBasketItem->getArticle(true);\n\n if (\\OxidEsales\\Eshop\\\
Core\\Registry::getConfig()->getConfigParam('bl_perfLoadSelectLists')) {\n \
\ // marking chosen select list\n $aSelList\
\ = $oBasketItem->getSelList();\n if (is_array($aSelList) &&\
\ ($aSelectlist = $oProduct->getSelectLists($sItemKey))) {\n \
\ reset($aSelList);\n foreach ($aSelList as $conkey\
\ => $iSel) {\n $aSelectlist[$conkey][$iSel]->selected\
\ = 1;\n }\n $oProduct->setSelectlist($aSelectlist);\n\
\ }\n }\n } catch (\\OxidEsales\\\
Eshop\\Core\\Exception\\NoArticleException $oEx) {\n \\OxidEsales\\\
Eshop\\Core\\Registry::getUtilsView()->addErrorToDisplay($oEx);\n \
\ $this->removeItem($sItemKey);\n $this->calculateBasket(true);\n\
\ continue;\n } catch (\\OxidEsales\\Eshop\\Core\\Exception\\\
ArticleInputException $oEx) {\n \\OxidEsales\\Eshop\\Core\\Registry::getUtilsView()->addErrorToDisplay($oEx);\n\
\ $this->removeItem($sItemKey);\n $this->calculateBasket(true);\n\
\ continue;\n }\n\n $aBasketArticles[$sItemKey]\
\ = $oProduct;\n }\n\n return $aBasketArticles;\n }"
- source_sentence: get test root
sentences:
- "@CheckReturnValue\n @SchedulerSupport(SchedulerSupport.NONE)\n public final\
\ Maybe<T> doOnDispose(Action onDispose) {\n return RxJavaPlugins.onAssembly(new\
\ MaybePeek<T>(this,\n Functions.emptyConsumer(), // onSubscribe\n\
\ Functions.emptyConsumer(), // onSuccess\n Functions.emptyConsumer(),\
\ // onError\n Functions.EMPTY_ACTION, // onComplete\n \
\ Functions.EMPTY_ACTION, // (onSuccess | onError | onComplete) after\n\
\ ObjectHelper.requireNonNull(onDispose, \"onDispose is null\"\
)\n ));\n }"
- "protected Object parseKeyElement(Element keyEle, BeanDefinition bd, String defaultKeyTypeName)\
\ {\n NodeList nl = keyEle.getChildNodes();\n Element subElement = null;\n\
\ for (int i = 0; i < nl.getLength(); i++) {\n Node node = nl.item(i);\n\
\ if (node instanceof Element) {\n // Child element is what we're\
\ looking for.\n if (subElement != null)\n error(\"<key> element\
\ must not contain more than one value sub-element\", keyEle);\n else subElement\
\ = (Element) node;\n }\n }\n return parsePropertySubElement(subElement,\
\ bd, defaultKeyTypeName);\n }"
- "function getRootPath(){\n var rootPath = path.resolve('.');\n while(rootPath){\n\
\ if(fs.existsSync(rootPath + '/config.json')){\n break;\n \
\ }\n rootPath = rootPath.substring(0, rootPath.lastIndexOf(path.sep));\n\
\ }\n return rootPath;\n}"
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on Shuu12121/CodeModernBERT-Owl-3.0
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Shuu12121/CodeModernBERT-Owl-3.0](https://huggingface.co/Shuu12121/CodeModernBERT-Owl-3.0). It maps sentences & paragraphs to a 768-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:** [Shuu12121/CodeModernBERT-Owl-3.0](https://huggingface.co/Shuu12121/CodeModernBERT-Owl-3.0) <!-- at revision a6beebbd776ae122f34f875dfa731557a1f70d8f -->
- **Maximum Sequence Length:** 1024 tokens
- **Output Dimensionality:** 768 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': 1024, 'do_lower_case': False}) with Transformer model: ModernBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## 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 = [
'get test root',
"function getRootPath(){\n var rootPath = path.resolve('.');\n while(rootPath){\n if(fs.existsSync(rootPath + '/config.json')){\n break;\n }\n rootPath = rootPath.substring(0, rootPath.lastIndexOf(path.sep));\n }\n return rootPath;\n}",
'protected Object parseKeyElement(Element keyEle, BeanDefinition bd, String defaultKeyTypeName) {\n NodeList nl = keyEle.getChildNodes();\n Element subElement = null;\n for (int i = 0; i < nl.getLength(); i++) {\n Node node = nl.item(i);\n if (node instanceof Element) {\n // Child element is what we\'re looking for.\n if (subElement != null)\n error("<key> element must not contain more than one value sub-element", keyEle);\n else subElement = (Element) node;\n }\n }\n return parsePropertySubElement(subElement, bd, defaultKeyTypeName);\n }',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### 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: 7,059,200 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 3 tokens</li><li>mean: 51.42 tokens</li><li>max: 974 tokens</li></ul> | <ul><li>min: 29 tokens</li><li>mean: 162.71 tokens</li><li>max: 1024 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:---------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| <code>// SetDefaultVersionId sets the DefaultVersionId field's value.</code> | <code>func (s *Policy) SetDefaultVersionId(v string) *Policy {<br> s.DefaultVersionId = &v<br> return s<br>}</code> | <code>1.0</code> |
| <code>// SetNextPageToken sets the NextPageToken field's value.</code> | <code>func (s *ListBudgetsForResourceOutput) SetNextPageToken(v string) *ListBudgetsForResourceOutput {<br> s.NextPageToken = &v<br> return s<br>}</code> | <code>1.0</code> |
| <code>// SetHealthyThresholdCount sets the HealthyThresholdCount field's value.</code> | <code>func (s *TargetGroup) SetHealthyThresholdCount(v int64) *TargetGroup {<br> s.HealthyThresholdCount = &v<br> return s<br>}</code> | <code>1.0</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 200
- `per_device_eval_batch_size`: 200
- `fp16`: True
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 200
- `per_device_eval_batch_size`: 200
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 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}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `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
- `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
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss |
|:------:|:------:|:-------------:|
| 0.0142 | 500 | 1.1661 |
| 0.0283 | 1000 | 0.1176 |
| 0.0425 | 1500 | 0.1096 |
| 0.0567 | 2000 | 0.1013 |
| 0.0708 | 2500 | 0.0967 |
| 0.0850 | 3000 | 0.0912 |
| 0.0992 | 3500 | 0.0886 |
| 0.1133 | 4000 | 0.0799 |
| 0.1275 | 4500 | 0.0776 |
| 0.1417 | 5000 | 0.0757 |
| 0.1558 | 5500 | 0.0751 |
| 0.1700 | 6000 | 0.0714 |
| 0.1842 | 6500 | 0.0703 |
| 0.1983 | 7000 | 0.0667 |
| 0.2125 | 7500 | 0.0674 |
| 0.2267 | 8000 | 0.0625 |
| 0.2408 | 8500 | 0.0598 |
| 0.2550 | 9000 | 0.0597 |
| 0.2692 | 9500 | 0.0585 |
| 0.2833 | 10000 | 0.0568 |
| 0.2975 | 10500 | 0.055 |
| 0.3117 | 11000 | 0.0554 |
| 0.3258 | 11500 | 0.0529 |
| 0.3400 | 12000 | 0.0516 |
| 0.3541 | 12500 | 0.0506 |
| 0.3683 | 13000 | 0.05 |
| 0.3825 | 13500 | 0.0484 |
| 0.3966 | 14000 | 0.0472 |
| 0.4108 | 14500 | 0.0468 |
| 0.4250 | 15000 | 0.045 |
| 0.4391 | 15500 | 0.046 |
| 0.4533 | 16000 | 0.0452 |
| 0.4675 | 16500 | 0.0428 |
| 0.4816 | 17000 | 0.0424 |
| 0.4958 | 17500 | 0.04 |
| 0.5100 | 18000 | 0.0402 |
| 0.5241 | 18500 | 0.0391 |
| 0.5383 | 19000 | 0.0389 |
| 0.5525 | 19500 | 0.0385 |
| 0.5666 | 20000 | 0.0357 |
| 0.5808 | 20500 | 0.0362 |
| 0.5950 | 21000 | 0.0369 |
| 0.6091 | 21500 | 0.0372 |
| 0.6233 | 22000 | 0.0351 |
| 0.6375 | 22500 | 0.034 |
| 0.6516 | 23000 | 0.0364 |
| 0.6658 | 23500 | 0.033 |
| 0.6800 | 24000 | 0.0336 |
| 0.6941 | 24500 | 0.0302 |
| 0.7083 | 25000 | 0.0309 |
| 0.7225 | 25500 | 0.0306 |
| 0.7366 | 26000 | 0.0316 |
| 0.7508 | 26500 | 0.0306 |
| 0.7650 | 27000 | 0.0307 |
| 0.7791 | 27500 | 0.0303 |
| 0.7933 | 28000 | 0.028 |
| 0.8075 | 28500 | 0.0289 |
| 0.8216 | 29000 | 0.0297 |
| 0.8358 | 29500 | 0.0281 |
| 0.8500 | 30000 | 0.029 |
| 0.8641 | 30500 | 0.027 |
| 0.8783 | 31000 | 0.0282 |
| 0.8925 | 31500 | 0.0264 |
| 0.9066 | 32000 | 0.027 |
| 0.9208 | 32500 | 0.0259 |
| 0.9350 | 33000 | 0.0272 |
| 0.9491 | 33500 | 0.0275 |
| 0.9633 | 34000 | 0.0244 |
| 0.9774 | 34500 | 0.0254 |
| 0.9916 | 35000 | 0.0261 |
| 1.0058 | 35500 | 0.0189 |
| 1.0199 | 36000 | 0.0118 |
| 1.0341 | 36500 | 0.012 |
| 1.0483 | 37000 | 0.0118 |
| 1.0624 | 37500 | 0.0109 |
| 1.0766 | 38000 | 0.0123 |
| 1.0908 | 38500 | 0.0122 |
| 1.1049 | 39000 | 0.0122 |
| 1.1191 | 39500 | 0.0123 |
| 1.1333 | 40000 | 0.0117 |
| 1.1474 | 40500 | 0.0115 |
| 1.1616 | 41000 | 0.0122 |
| 1.1758 | 41500 | 0.0117 |
| 1.1899 | 42000 | 0.0119 |
| 1.2041 | 42500 | 0.0112 |
| 1.2183 | 43000 | 0.0122 |
| 1.2324 | 43500 | 0.0116 |
| 1.2466 | 44000 | 0.0107 |
| 1.2608 | 44500 | 0.0126 |
| 1.2749 | 45000 | 0.0114 |
| 1.2891 | 45500 | 0.011 |
| 1.3033 | 46000 | 0.0116 |
| 1.3174 | 46500 | 0.0114 |
| 1.3316 | 47000 | 0.0111 |
| 1.3458 | 47500 | 0.0112 |
| 1.3599 | 48000 | 0.0112 |
| 1.3741 | 48500 | 0.0115 |
| 1.3883 | 49000 | 0.0104 |
| 1.4024 | 49500 | 0.0109 |
| 1.4166 | 50000 | 0.0113 |
| 1.4308 | 50500 | 0.0115 |
| 1.4449 | 51000 | 0.0103 |
| 1.4591 | 51500 | 0.0114 |
| 1.4733 | 52000 | 0.0104 |
| 1.4874 | 52500 | 0.0106 |
| 1.5016 | 53000 | 0.0103 |
| 1.5158 | 53500 | 0.0102 |
| 1.5299 | 54000 | 0.0101 |
| 1.5441 | 54500 | 0.0104 |
| 1.5583 | 55000 | 0.011 |
| 1.5724 | 55500 | 0.0107 |
| 1.5866 | 56000 | 0.0097 |
| 1.6007 | 56500 | 0.0099 |
| 1.6149 | 57000 | 0.0102 |
| 1.6291 | 57500 | 0.0098 |
| 1.6432 | 58000 | 0.01 |
| 1.6574 | 58500 | 0.0096 |
| 1.6716 | 59000 | 0.0099 |
| 1.6857 | 59500 | 0.0103 |
| 1.6999 | 60000 | 0.0098 |
| 1.7141 | 60500 | 0.0097 |
| 1.7282 | 61000 | 0.0094 |
| 1.7424 | 61500 | 0.0093 |
| 1.7566 | 62000 | 0.0102 |
| 1.7707 | 62500 | 0.0099 |
| 1.7849 | 63000 | 0.0098 |
| 1.7991 | 63500 | 0.009 |
| 1.8132 | 64000 | 0.0097 |
| 1.8274 | 64500 | 0.009 |
| 1.8416 | 65000 | 0.0093 |
| 1.8557 | 65500 | 0.0092 |
| 1.8699 | 66000 | 0.0095 |
| 1.8841 | 66500 | 0.0093 |
| 1.8982 | 67000 | 0.0094 |
| 1.9124 | 67500 | 0.0089 |
| 1.9266 | 68000 | 0.0091 |
| 1.9407 | 68500 | 0.0089 |
| 1.9549 | 69000 | 0.0084 |
| 1.9691 | 69500 | 0.0087 |
| 1.9832 | 70000 | 0.0094 |
| 1.9974 | 70500 | 0.0085 |
| 2.0116 | 71000 | 0.0049 |
| 2.0257 | 71500 | 0.0041 |
| 2.0399 | 72000 | 0.0039 |
| 2.0541 | 72500 | 0.0038 |
| 2.0682 | 73000 | 0.004 |
| 2.0824 | 73500 | 0.0039 |
| 2.0966 | 74000 | 0.0038 |
| 2.1107 | 74500 | 0.0041 |
| 2.1249 | 75000 | 0.0037 |
| 2.1391 | 75500 | 0.0038 |
| 2.1532 | 76000 | 0.0041 |
| 2.1674 | 76500 | 0.0036 |
| 2.1816 | 77000 | 0.0039 |
| 2.1957 | 77500 | 0.0039 |
| 2.2099 | 78000 | 0.0038 |
| 2.2240 | 78500 | 0.0038 |
| 2.2382 | 79000 | 0.0037 |
| 2.2524 | 79500 | 0.0037 |
| 2.2665 | 80000 | 0.0036 |
| 2.2807 | 80500 | 0.0038 |
| 2.2949 | 81000 | 0.0037 |
| 2.3090 | 81500 | 0.0036 |
| 2.3232 | 82000 | 0.0036 |
| 2.3374 | 82500 | 0.0038 |
| 2.3515 | 83000 | 0.0037 |
| 2.3657 | 83500 | 0.0037 |
| 2.3799 | 84000 | 0.0038 |
| 2.3940 | 84500 | 0.0037 |
| 2.4082 | 85000 | 0.0036 |
| 2.4224 | 85500 | 0.0034 |
| 2.4365 | 86000 | 0.0035 |
| 2.4507 | 86500 | 0.0033 |
| 2.4649 | 87000 | 0.0036 |
| 2.4790 | 87500 | 0.0035 |
| 2.4932 | 88000 | 0.0034 |
| 2.5074 | 88500 | 0.0034 |
| 2.5215 | 89000 | 0.0034 |
| 2.5357 | 89500 | 0.0031 |
| 2.5499 | 90000 | 0.0033 |
| 2.5640 | 90500 | 0.0033 |
| 2.5782 | 91000 | 0.0035 |
| 2.5924 | 91500 | 0.0033 |
| 2.6065 | 92000 | 0.0032 |
| 2.6207 | 92500 | 0.0034 |
| 2.6349 | 93000 | 0.0031 |
| 2.6490 | 93500 | 0.0032 |
| 2.6632 | 94000 | 0.0032 |
| 2.6774 | 94500 | 0.0033 |
| 2.6915 | 95000 | 0.0032 |
| 2.7057 | 95500 | 0.003 |
| 2.7199 | 96000 | 0.0032 |
| 2.7340 | 96500 | 0.0032 |
| 2.7482 | 97000 | 0.003 |
| 2.7624 | 97500 | 0.0032 |
| 2.7765 | 98000 | 0.0033 |
| 2.7907 | 98500 | 0.003 |
| 2.8049 | 99000 | 0.003 |
| 2.8190 | 99500 | 0.0031 |
| 2.8332 | 100000 | 0.0031 |
| 2.8473 | 100500 | 0.003 |
| 2.8615 | 101000 | 0.003 |
| 2.8757 | 101500 | 0.003 |
| 2.8898 | 102000 | 0.003 |
| 2.9040 | 102500 | 0.003 |
| 2.9182 | 103000 | 0.003 |
| 2.9323 | 103500 | 0.003 |
| 2.9465 | 104000 | 0.0033 |
| 2.9607 | 104500 | 0.0029 |
| 2.9748 | 105000 | 0.003 |
| 2.9890 | 105500 | 0.0028 |
</details>
### Framework Versions
- Python: 3.11.13
- Sentence Transformers: 4.1.0
- Transformers: 4.52.4
- PyTorch: 2.6.0+cu124
- Accelerate: 1.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## 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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