Upload 11 files

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 +1,590 @@
+---
+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}
+}
+```
+
+<!--
+## 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.*
+-->

adapter_config.json

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+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "unsloth/all-MiniLM-L6-v2",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.18.1",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "query",
+    "dense",
+    "value",
+    "key"
+  ],
+  "target_parameters": null,
+  "task_type": "FEATURE_EXTRACTION",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c51b169756ed563e668e3474b58282b9ec2d2b4fa73a351043aae43dc6e92082
+size 10823376

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "sentence_transformers": "5.1.1",
+    "transformers": "4.57.1",
+    "pytorch": "2.10.0+cu128"
+  },
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

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
+    },
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+    },
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+      "rstrip": false,
+      "single_word": false,
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+    "102": {
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+      "lstrip": false,
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+      "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]"
+}