--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:200000 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: BAAI/bge-base-en-v1.5 widget: - source_sentence: Sets the global variables $rects and $origRectSpecs sentences: - "def modify_ranking(tournament):\n database = TinyDB('db.json')\n # recuperation\ \ de tous les joueurs du tournoi\n players_table = database.table('players')\n\ \ list_players = get_player_list(tournament)\n # Modification du rang joueur\ \ par joueur\n for player in list_players:\n new_ranking = view.modify_ranking_view(player)\n\ \ players_table.update({'Classement': new_ranking}, doc_ids=[player.doc_id])" - "function setConstants() {\n const wrapItems = \".image-analysis-wrapper\ \ .face-wrap, .image-analysis-wrapper .score-wrap, .image-analysis-wrapper .attribute-wrap,\ \ .image-analysis-wrapper .region-block, .image-analysis-wrapper .region-block\ \ .word-block .word-wrap\";\n\n $rects = jQuery(\".image-analysis-wrapper\ \ .rectangle\");\n\n // Iterate over each rectangle and save the width,\ \ height, top position,\n // left position, closest stats block element,\ \ and position of the closest\n // stats block element to an object. Each\ \ object is then added to the \n // $origRectSpecs array for global use.\n\ \ $origRectSpecs = $rects.map(function () {\n closestWrapItems\ \ = jQuery(this).siblings(wrapItems);\n\n const stats = closestWrapItems.map(function\ \ () {\n return {\n origStatTop: jQuery(this).position().top\ \ || parseInt(jQuery(this).css(\"top\")),\n origStatLeft: jQuery(this).position().left\ \ || parseInt(jQuery(this).css(\"left\"))\n }\n })\n\ \n return {\n origRectWidth: jQuery(this).width(),\n\ \ origRectHeight: jQuery(this).height(),\n origRectTop:\ \ jQuery(this).position().top || parseInt(jQuery(this).css(\"top\")), // if the\ \ rect is on a tab that is currently not displayed it has a position of 0, so\ \ this check gets the css instead so we don't lose the value\n \ \ origRectLeft: jQuery(this).position().left || parseInt(jQuery(this).css(\"left\"\ )),\n statBlock: closestWrapItems[0],\n statPosition:\ \ stats[0]\n }\n })\n }" - "reset() {\n\n // Set the initial crop to match any given fixed aspect\ \ ratio (or\n // default to a square crop 1:1).\n let aspectRatio\ \ = this._initialAspectRatio\n\n // Calculate the initial crop size such\ \ that it fits within the bounds\n let width = getWidth(this.bounds)\n\ \ let height = getWidth(this.bounds) / aspectRatio\n\n if (aspectRatio\ \ < width / getHeight(this.bounds)) {\n width = getHeight(this.bounds)\ \ * aspectRatio\n height = getHeight(this.bounds)\n }\n\n \ \ // Calculate the initial crop position to be central to the bounds\n \ \ const x = (getWidth(this.bounds) - width) / 2\n const y = (getHeight(this.bounds)\ \ - height) / 2\n\n // Set the region\n this.region = [\n \ \ [x, y],\n [x + width, y + height]\n ]\n }" - source_sentence: Returns true if the motor is at its lower limit. sentences: - "def is_lower_limit(self):\n is_lower = self.get_raw_status() & self.STATUS_LLIM\n\ \ return bool(is_lower)" - "def testLowerBound(self,time,accel):\n\t\tif (time - self.timestamp) > ParserSettings.TIME_DELTA:#tests\ \ lockout threshold of a flick event\n\t\t\tif accel > self.lower:#tests to see\ \ if the flick maximum is met yet, relative to the previous magnitude\n\t\t\t\t\ self.timestamp \t= time#set appropriate values when flick triggered\n\t\t\t\t\ toReturn \t\t= self.lower\n\t\t\t\tself.lower\t \t= 0#reset flick for next magnitude\ \ test\n\t\t\t\treturn toReturn\n\t\t\telse:\n\t\t\t\tself.lower = accel#if no\ \ flick yet, update most recent flick to test\n\t\t\t\treturn 0\n\t\telse:\n\t\ \t\treturn 0" - "function checkVisibility(message)\n{\n\t// Scroll the view down a certain amount\n\ \t$chatlogs.stop().animate({scrollTop: $chatlogs[0].scrollHeight});\n}" - source_sentence: Extracts an object of params the given route cares about from the given params object. sentences: - "function getRouteParams(route, params) {\n\t var routeParams = {};\n\t\n\t \ \ if (!route.path) return routeParams;\n\t\n\t (0, _PatternUtils.getParamNames)(route.path).forEach(function\ \ (p) {\n\t if (Object.prototype.hasOwnProperty.call(params, p)) {\n\t \ \ routeParams[p] = params[p];\n\t }\n\t });\n\t\n\t return routeParams;\n\ \t}" - "public static void initImageLoader(Context context) {\n\t\tFile cacheDir = StorageUtils.getCacheDirectory(context);\n\ \t\tImageLoaderConfiguration config = new ImageLoaderConfiguration.Builder(context)\n\ \t\t\t\t.threadPriority(Thread.NORM_PRIORITY - 2)\n\t\t\t\t.denyCacheImageMultipleSizesInMemory()\ \ \n//\t\t\t\t.discCache(new UnlimitedDiscCache(cacheDir)) // default\n\t\t\t\t\ .discCacheFileNameGenerator(new Md5FileNameGenerator())\n\t\t\t\t.memoryCache(new\ \ LruMemoryCache(2 * 1024 * 1024))\n\t\t\t\t.tasksProcessingOrder(QueueProcessingType.LIFO)\n\ \t\t\t\t.writeDebugLogs() // Remove for release app\t\t\t\t\n\t\t\t\t.build();\n\ \t\t// Initialize ImageLoader with configuration.\n\t\tImageLoader.getInstance().init(config);\n\ \t}" - "function getRouteParams(route, params) {\n\t var routeParams = {};\n\n\t if\ \ (!route.path) return routeParams;\n\n\t (0, _PatternUtils.getParamNames)(route.path).forEach(function\ \ (p) {\n\t if (Object.prototype.hasOwnProperty.call(params, p)) {\n\t \ \ routeParams[p] = params[p];\n\t }\n\t });\n\n\t return routeParams;\n\t\ }" - source_sentence: select all as they all have a class of "section" return array of IDs sentences: - "func (o *BookBuyOK) WriteResponse(rw http.ResponseWriter, producer runtime.Producer)\ \ {\n\n\trw.WriteHeader(200)\n\tif o.Payload != nil {\n\t\tpayload := o.Payload\n\ \t\tif err := producer.Produce(rw, payload); err != nil {\n\t\t\tpanic(err) //\ \ let the recovery middleware deal with this\n\t\t}\n\t}\n}" - "function getMenuCLassesIDs()\n {\n let IdsArray = [];\n const\ \ elements = document.querySelectorAll('.banner,.section');\n for(const\ \ elm of elements)\n {\n IdsArray.push(elm.id);\n \ \ }\n \n return IdsArray;\n }" - "getAllSectionsForRegion(region) {\n if (!region) {\n return\ \ null;\n }\n return region.getElementsByClassName(A11yClassNames.SECTION);\n\ \ }" - source_sentence: For component xyz, returns "xyz[]" sentences: - "public String toString() {\n\t \tif(size == 0) {\n\t \t\treturn \"[]\"\ ;\n\t \t}else {\n\t \t\t\n\t \t\tString result = \"[\" + elementData[0];\n\ \t \t\tfor(int i = 1; i < size; i++) {\n\t \t\t\tresult += \", \" + elementData[i];\n\ \t \t\t}\n\t \t\t\n\t \t\tresult += \"]\";\n\t \t\t\n\t \t\treturn\ \ result;\n\t \t}\n\t }" - "def masterPath(self):\n\t\treturn fl.File( self._path + '/master.data' )" - "private static final String getArrayTypeName(String typeName) {\n final String\ \ arrayTypeName = builtInArrayComponentName2ArrayTypeName.get(typeName);\n \ \ return (null == arrayTypeName) ? typeName + ARRAY_TYPE_SUFFIX : arrayTypeName;\n\ \ }" pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@1 - cosine_mrr@5 - cosine_mrr@10 - cosine_map@100 model-index: - name: SentenceTransformer based on BAAI/bge-base-en-v1.5 results: - task: type: information-retrieval name: Information Retrieval dataset: name: cornstack eval type: cornstack_eval metrics: - type: cosine_accuracy@1 value: 0.718 name: Cosine Accuracy@1 - type: cosine_accuracy@5 value: 0.884 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.924 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.718 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2866666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1768 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09240000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.718 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.86 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.884 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.924 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8259696432548304 name: Cosine Ndcg@10 - type: cosine_mrr@1 value: 0.718 name: Cosine Mrr@1 - type: cosine_mrr@5 value: 0.7882666666666666 name: Cosine Mrr@5 - type: cosine_mrr@10 value: 0.7940396825396825 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7967267778119025 name: Cosine Map@100 --- # SentenceTransformer based on BAAI/bge-base-en-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for retrieval. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) - **Maximum Sequence Length:** 384 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Supported Modality:** Text ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'}) (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'cls', '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 queries = [ 'For component xyz, returns "xyz[]"', ] documents = [ 'private static final String getArrayTypeName(String typeName) {\n final String arrayTypeName = builtInArrayComponentName2ArrayTypeName.get(typeName);\n return (null == arrayTypeName) ? typeName + ARRAY_TYPE_SUFFIX : arrayTypeName;\n }', 'public String toString() {\n\t \tif(size == 0) {\n\t \t\treturn "[]";\n\t \t}else {\n\t \t\t\n\t \t\tString result = "[" + elementData[0];\n\t \t\tfor(int i = 1; i < size; i++) {\n\t \t\t\tresult += ", " + elementData[i];\n\t \t\t}\n\t \t\t\n\t \t\tresult += "]";\n\t \t\t\n\t \t\treturn result;\n\t \t}\n\t }', ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) print(query_embeddings.shape, document_embeddings.shape) # [1, 768] [2, 768] # Get the similarity scores for the embeddings similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) # tensor([[0.4454, 0.3780]]) ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `cornstack_eval` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:----------| | cosine_accuracy@1 | 0.718 | | cosine_accuracy@5 | 0.884 | | cosine_accuracy@10 | 0.924 | | cosine_precision@1 | 0.718 | | cosine_precision@3 | 0.2867 | | cosine_precision@5 | 0.1768 | | cosine_precision@10 | 0.0924 | | cosine_recall@1 | 0.718 | | cosine_recall@3 | 0.86 | | cosine_recall@5 | 0.884 | | cosine_recall@10 | 0.924 | | **cosine_ndcg@10** | **0.826** | | cosine_mrr@1 | 0.718 | | cosine_mrr@5 | 0.7883 | | cosine_mrr@10 | 0.794 | | cosine_map@100 | 0.7967 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 200,000 training samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 100 samples: | | anchor | positive | negative | |:---------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | string | | modality | text | text | text | | details | | | | * Samples: | anchor | positive | negative | |:--------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Fades all outputs to the given color and waits for it to complete. | def FadeOutputs(box, color, steps=50):
for output in box:
output.Fade(color=color, steps=steps)
time.sleep(steps / (float(box.frequency) / len(box))) | def _colour_loop(self, colours, seconds=None, milliseconds=None, fade=True):
colours = self.convert_to_colour_list(colours) #Forces a list of colours into an actual python list
if len(colours)<2:
colours.append("#000000") #Blink between black and the specified colour if only one provided

#Start with the first colour immediately:
if fade:
self.fade(colours[0])
else:
self.set(colours[0])
step_time = self.clean_time_in_milliseconds(seconds, milliseconds, default_seconds=1, minimum_milliseconds=50)

#Do the loop
i = 1 #We're moving to the second colour now
total_colours = len(colours)
while not self._sequence_stop_signal:
#Resolve our colour
next_colour = colours[i]
i = (i+1) % total_colours #ensures we are never asking for more colours than provided
if fade: #Fading is a blocking process, thus we let the fade l... | | Sets the additional element count if buffer resize is required, defaults to initialElementCount of factory method. | public void setResizeElementCount(int v) { vboSet.setResizeElementCount(v); } | public int getResizeElementCount() { return vboSet.getResizeElementCount(); } | | delete a specific incident | def delete_specific_incident(self, incident_id):
self.cursor.execute("""DELETE FROM incidents WHERE incident_id ='%s' AND status='draft'
""" %(incident_id))
self.commiting()
return incident_id | def delete(openstack_resource):
openstack_resource.delete() | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128 ], "matryoshka_weights": [ 1.0, 1.0, 1.0, 1.0 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 16 - `gradient_accumulation_steps`: 4 - `learning_rate`: 2e-05 - `num_train_epochs`: 1 - `warmup_ratio`: 0.05 - `fp16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `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`: 2e-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`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: None - `warmup_ratio`: 0.05 - `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`: {}
### Training Logs | Epoch | Step | Training Loss | cornstack_eval_cosine_ndcg@10 | |:-----:|:----:|:-------------:|:-----------------------------:| | 0.016 | 50 | 4.7419 | - | | 0.032 | 100 | 4.2347 | - | | 0.048 | 150 | 3.9702 | - | | 0.064 | 200 | 3.7572 | - | | 0.08 | 250 | 3.7142 | - | | 0.096 | 300 | 3.5756 | - | | 0.112 | 350 | 3.6356 | - | | 0.128 | 400 | 3.7447 | - | | 0.144 | 450 | 3.4957 | - | | 0.16 | 500 | 3.4753 | - | | 0.176 | 550 | 3.3913 | - | | 0.192 | 600 | 3.1695 | - | | 0.208 | 650 | 3.2787 | - | | 0.224 | 700 | 3.2488 | - | | 0.24 | 750 | 3.2078 | - | | 0.256 | 800 | 3.2942 | - | | 0.272 | 850 | 3.0672 | - | | 0.288 | 900 | 3.1279 | - | | 0.304 | 950 | 3.1464 | - | | 0.32 | 1000 | 3.1526 | 0.8336 | | 0.336 | 1050 | 3.1064 | - | | 0.352 | 1100 | 3.2408 | - | | 0.368 | 1150 | 3.2562 | - | | 0.384 | 1200 | 3.1835 | - | | 0.4 | 1250 | 2.9471 | - | | 0.416 | 1300 | 3.1631 | - | | 0.432 | 1350 | 3.1428 | - | | 0.448 | 1400 | 2.9445 | - | | 0.464 | 1450 | 3.0482 | - | | 0.48 | 1500 | 3.09 | - | | 0.496 | 1550 | 3.0184 | - | | 0.512 | 1600 | 3.031 | - | | 0.528 | 1650 | 2.9703 | - | | 0.544 | 1700 | 2.9743 | - | | 0.56 | 1750 | 3.0344 | - | | 0.576 | 1800 | 3.0521 | - | | 0.592 | 1850 | 2.9177 | - | | 0.608 | 1900 | 3.0357 | - | | 0.624 | 1950 | 2.9277 | - | | 0.64 | 2000 | 2.8525 | 0.8287 | | 0.656 | 2050 | 2.978 | - | | 0.672 | 2100 | 2.8437 | - | | 0.688 | 2150 | 2.849 | - | | 0.704 | 2200 | 2.9248 | - | | 0.72 | 2250 | 2.8551 | - | | 0.736 | 2300 | 2.8741 | - | | 0.752 | 2350 | 2.7847 | - | | 0.768 | 2400 | 2.8682 | - | | 0.784 | 2450 | 2.7556 | - | | 0.8 | 2500 | 2.8122 | - | | 0.816 | 2550 | 2.9173 | - | | 0.832 | 2600 | 2.8772 | - | | 0.848 | 2650 | 2.8275 | - | | 0.864 | 2700 | 2.7819 | - | | 0.88 | 2750 | 2.8215 | - | | 0.896 | 2800 | 2.9207 | - | | 0.912 | 2850 | 2.8892 | - | | 0.928 | 2900 | 2.7682 | - | | 0.944 | 2950 | 2.868 | - | | 0.96 | 3000 | 2.7926 | 0.8260 | | 0.976 | 3050 | 2.8871 | - | | 0.992 | 3100 | 2.8142 | - | ### Training Time - **Training**: 2.5 days - **Evaluation**: 1.9 minutes - **Total**: 2.5 days ### Framework Versions - Python: 3.13.7 - Sentence Transformers: 5.6.0 - Transformers: 4.57.6 - PyTorch: 2.12.1+cu126 - Accelerate: 1.13.0 - Datasets: 4.8.5 - Tokenizers: 0.22.2 ## 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", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{oord2019representationlearningcontrastivepredictive, title={Representation Learning with Contrastive Predictive Coding}, author={Aaron van den Oord and Yazhe Li and Oriol Vinyals}, year={2019}, eprint={1807.03748}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/1807.03748}, } ```