| ---
|
| 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) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
|
| - **Maximum Sequence Length:** 384 tokens
|
| - **Output Dimensionality:** 768 dimensions
|
| - **Similarity Function:** Cosine Similarity
|
| - **Supported Modality:** Text
|
| <!-- - **Training Dataset:** Unknown -->
|
| <!-- - **Language:** Unknown -->
|
| <!-- - **License:** Unknown -->
|
|
|
| ### 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]])
|
| ```
|
| <!--
|
| ### 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.*
|
| -->
|
|
|
| ## Evaluation
|
|
|
| ### Metrics
|
|
|
| #### Information Retrieval
|
|
|
| * Dataset: `cornstack_eval`
|
| * Evaluated with [<code>InformationRetrievalEvaluator</code>](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 |
|
|
|
| <!--
|
| ## 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: 200,000 training samples
|
| * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
|
| * Approximate statistics based on the first 100 samples:
|
| | | anchor | positive | negative |
|
| |:---------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
|
| | type | string | string | string |
|
| | modality | text | text | text |
|
| | details | <ul><li>min: 6 tokens</li><li>mean: 19.83 tokens</li><li>max: 105 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 139.33 tokens</li><li>max: 384 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 100.52 tokens</li><li>max: 384 tokens</li></ul> |
|
| * Samples:
|
| | anchor | positive | negative |
|
| |:--------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
| | <code>Fades all outputs to the given color and waits for it to complete.</code> | <code>def FadeOutputs(box, color, steps=50):<br> for output in box:<br> output.Fade(color=color, steps=steps)<br> time.sleep(steps / (float(box.frequency) / len(box)))</code> | <code>def _colour_loop(self, colours, seconds=None, milliseconds=None, fade=True):<br> colours = self.convert_to_colour_list(colours) #Forces a list of colours into an actual python list<br> if len(colours)<2:<br> colours.append("#000000") #Blink between black and the specified colour if only one provided<br> <br> #Start with the first colour immediately:<br> if fade:<br> self.fade(colours[0])<br> else:<br> self.set(colours[0])<br> step_time = self.clean_time_in_milliseconds(seconds, milliseconds, default_seconds=1, minimum_milliseconds=50)<br> <br> #Do the loop<br> i = 1 #We're moving to the second colour now<br> total_colours = len(colours)<br> while not self._sequence_stop_signal:<br> #Resolve our colour<br> next_colour = colours[i]<br> i = (i+1) % total_colours #ensures we are never asking for more colours than provided<br> if fade: #Fading is a blocking process, thus we let the fade l...</code> |
|
| | <code>Sets the additional element count if buffer resize is required, defaults to initialElementCount of factory method.</code> | <code>public void setResizeElementCount(int v) { vboSet.setResizeElementCount(v); }</code> | <code>public int getResizeElementCount() { return vboSet.getResizeElementCount(); }</code> |
|
| | <code>delete a specific incident</code> | <code>def delete_specific_incident(self, incident_id):<br> self.cursor.execute("""DELETE FROM incidents WHERE incident_id ='%s' AND status='draft'<br> """ %(incident_id))<br> self.commiting()<br> return incident_id</code> | <code>def delete(openstack_resource):<br> openstack_resource.delete()</code> |
|
| * Loss: [<code>MatryoshkaLoss</code>](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
|
| <details><summary>Click to expand</summary>
|
|
|
| - `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`: {}
|
|
|
| </details>
|
|
|
| ### 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},
|
| }
|
| ```
|
|
|
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|
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