| | --- |
| | tags: |
| | - sentence-transformers |
| | - sentence-similarity |
| | - feature-extraction |
| | - generated_from_trainer |
| | - dataset_size:6960000 |
| | - loss:MultipleNegativesRankingLoss |
| | base_model: Shuu12121/CodeModernBERT-Owl-4.1 |
| | widget: |
| | - source_sentence: Create a function to call with simple and hard types This is done |
| | so simple types don't need to check for hard types |
| | sentences: |
| | - "function copyHead(headHtml, doc) {\n var head = doc.getElementsByTagName('head')[0];\n\ |
| | \n if (head.innerHTML == headHtml) {\n // the content is already\ |
| | \ correct\n return;\n }\n\n jQuery.init(head).empty();\n\ |
| | \n appendHTML(headHtml, head);\n }" |
| | - "func compress(value float64) int16 {\n\ti := int16(precision*math.Log(1.0+math.Abs(value))\ |
| | \ + 0.5)\n\tif value < 0 {\n\t\treturn -1 * i\n\t}\n\treturn i\n}" |
| | - "function (types, hard) {\n for (var t in types) {\n \ |
| | \ if (types.hasOwnProperty(t)) {\n (function (prop) {\n \ |
| | \ Object.defineProperty(props, prop, {\n \ |
| | \ get: function () {\n for (var i =\ |
| | \ 0; i < inputs.length; ++i) {\n if (!checkType(prop,\ |
| | \ inputs[i], hard)) {\n return false;\n\ |
| | \ }\n }\n \ |
| | \ return true;\n }\n \ |
| | \ });\n }(t));\n }\n \ |
| | \ }\n }" |
| | - source_sentence: 'Takes an array of promises and returns a promise that is fulfilled |
| | once all |
| | |
| | the promises in the array are fulfilled |
| | |
| | |
| | @param {Array} array The array of promises |
| | |
| | @return {Promise} the promise that is fulfilled when all the array is fulfilled, |
| | resolved to the array of results' |
| | sentences: |
| | - "public static List<DockerImage> getDockerImagesFromAgents(final int buildInfoId,\ |
| | \ TaskListener listener) throws IOException, InterruptedException {\n List<DockerImage>\ |
| | \ dockerImages = new ArrayList<DockerImage>();\n\n // Collect images from\ |
| | \ the master:\n dockerImages.addAll(getAndDiscardImagesByBuildId(buildInfoId));\n\ |
| | \n // Collect images from all the agents:\n List<Node> nodes = Jenkins.getInstance().getNodes();\n\ |
| | \ for (Node node : nodes) {\n if (node == null || node.getChannel()\ |
| | \ == null) {\n continue;\n }\n try {\n \ |
| | \ List<DockerImage> partialDockerImages = node.getChannel().call(new\ |
| | \ MasterToSlaveCallable<List<DockerImage>, IOException>() {\n \ |
| | \ public List<DockerImage> call() throws IOException {\n \ |
| | \ List<DockerImage> dockerImages = new ArrayList<DockerImage>();\n \ |
| | \ dockerImages.addAll(getAndDiscardImagesByBuildId(buildInfoId));\n\ |
| | \ return dockerImages;\n }\n \ |
| | \ });\n dockerImages.addAll(partialDockerImages);\n \ |
| | \ } catch (Exception e) {\n listener.getLogger().println(\"\ |
| | Could not collect docker images from Jenkins node '\" + node.getDisplayName()\ |
| | \ + \"' due to: \" + e.getMessage());\n }\n }\n return\ |
| | \ dockerImages;\n }" |
| | - "public function findUnitByStart(Token $token) {\n\t\tforeach ($this->collection\ |
| | \ as $unit) {\n\t\t\tif ($unit->start === $token) {\n\t\t\t\treturn $unit;\n\t\ |
| | \t\t}\n\t\t}\n\n\t\treturn null;\n\t}" |
| | - "function (array) {\n var self = this,\n deferred =\ |
| | \ new Deferred(),\n fulfilled = 0,\n length,\n \ |
| | \ results = [],\n hasError = false;\n\n \ |
| | \ if (!isArray(array)) {\n array = slice.call(arguments);\n \ |
| | \ }\n length = array.length;\n\n if (length ===\ |
| | \ 0) {\n deferred.emitSuccess(results);\n } else {\n\ |
| | \ array.forEach(function (promise, index) {\n\n \ |
| | \ self.when(promise,\n //Success\n \ |
| | \ function (value) {\n results[index] = value;\n\ |
| | \ fulfilled += 1;\n if (fulfilled\ |
| | \ === length) {\n\n if (hasError) {\n \ |
| | \ deferred.emitError(results);\n \ |
| | \ } else {\n deferred.emitSuccess(results);\n\ |
| | \ }\n }\n \ |
| | \ },\n //Error\n function\ |
| | \ (error) {\n results[index] = error;\n \ |
| | \ hasError = true;\n fulfilled += 1;\n\ |
| | \ if (fulfilled === length) {\n \ |
| | \ deferred.emitError(results);\n }\n \ |
| | \ }\n );\n });\n \ |
| | \ }\n return deferred.getPromise();\n }" |
| | - source_sentence: 'Create and return a MBeanInfo instance for the supplied object. |
| | |
| | |
| | @param object Supplied object to inspect. |
| | |
| | @param classIntrospector ClassIntrospector to use. |
| | |
| | |
| | @return a MBeanInfo instance for the supplied object. |
| | |
| | |
| | @throws IntrospectionException If failed to create Info object |
| | |
| | @throws IllegalArgumentException' |
| | sentences: |
| | - "function toggleShepherdModalClass(currentElement) {\n const shepherdModal =\ |
| | \ document.querySelector(`${classNames.modalTarget}`);\n\n if (shepherdModal)\ |
| | \ {\n shepherdModal.classList.remove(classNames.modalTarget);\n }\n\n currentElement.classList.add(classNames.modalTarget);\n\ |
| | }" |
| | - "public function handle(): void\n {\n $directory = $this->argument('directory');\n\ |
| | \n if (!is_dir($directory)) {\n $this->error(\n \ |
| | \ sprintf('The directory \"%1$s\" does not exist. Run `resume make --output=%1$s`.',\ |
| | \ $directory)\n );\n\n exit(1);\n }\n\n chdir($directory);\n\ |
| | \n $this->info(\n sprintf('Resume preview started: http://%s:%s',\ |
| | \ $this->host(), $this->port())\n );\n $this->info('Stop the server\ |
| | \ with CTRL+C.');\n\n passthru($this->command($this->host(), $this->port()),\ |
| | \ $exitCode);\n\n exit($exitCode);\n }" |
| | - "private MBeanInfo getInfo(Object object, ClassIntrospector classIntrospector)\ |
| | \ throws IntrospectionException {\n JmxBean jmxBean = AnnotationUtils.getAnnotation(object.getClass(),\ |
| | \ JmxBean.class);\n \n MBeanInfo beanInfo = new MBeanInfo(object.getClass().getName(),\ |
| | \ \n jmxBean.description(), \n \ |
| | \ getAttributes(classIntrospector), \n \ |
| | \ getConstructors(classIntrospector),\ |
| | \ \n getOperations(classIntrospector),\ |
| | \ \n getNotifications(object));\n \ |
| | \ return beanInfo;\n }" |
| | - source_sentence: 'Adds a collaborator to this folder. |
| | |
| | |
| | @param collaborator the collaborator to add. |
| | |
| | @param role the role of the collaborator. |
| | |
| | @return info about the new collaboration.' |
| | sentences: |
| | - "final public function readUInt32()\n {\n if (PHP_INT_SIZE < 8) {\n\ |
| | \ // @codeCoverageIgnoreStart\n if ($this->isLittleEndian())\ |
| | \ {\n list(, $lo, $hi) = unpack('S*', $this->read(4));\n \ |
| | \ } else {\n list(, $hi, $lo) = unpack('S*', $this->read(4));\n\ |
| | \ }\n return $hi * (0xffff+1) + $lo; // eq $hi << 16 | $lo\n\ |
| | \ // @codeCoverageIgnoreEnd\n } else {\n list(, $int)\ |
| | \ = unpack('L*', $this->read(4)) + array(0, 0);\n return $int;\n \ |
| | \ }\n }" |
| | - "public function add_on_empty($attribute, $msg)\n\t{\n\t\tif (empty($msg))\n\t\ |
| | \t\t$msg = self::$DEFAULT_ERROR_MESSAGES['empty'];\n\n\t\tif (empty($this->model->$attribute))\n\ |
| | \t\t\t$this->add($attribute, $msg);\n\t}" |
| | - "public BoxCollaboration.Info collaborate(BoxCollaborator collaborator, BoxCollaboration.Role\ |
| | \ role) {\n JsonObject accessibleByField = new JsonObject();\n accessibleByField.add(\"\ |
| | id\", collaborator.getID());\n\n if (collaborator instanceof BoxUser) {\n\ |
| | \ accessibleByField.add(\"type\", \"user\");\n } else if (collaborator\ |
| | \ instanceof BoxGroup) {\n accessibleByField.add(\"type\", \"group\"\ |
| | );\n } else {\n throw new IllegalArgumentException(\"The given\ |
| | \ collaborator is of an unknown type.\");\n }\n\n return this.collaborate(accessibleByField,\ |
| | \ role, null, null);\n }" |
| | - source_sentence: 'Register the router instance. |
| | |
| | |
| | @return void' |
| | sentences: |
| | - "protected function registerRouter()\n\t{\n\t\t$this->app['router'] = $this->app->share(function($app)\n\ |
| | \t\t{\n\t\t\treturn new Router($app['events'], $app);\n\t\t});\n\t}" |
| | - "@Override\n @Nullable\n public Long apply(@Nonnull Long partialAccountNumber)\ |
| | \ {\n checkNotNull(partialAccountNumber, \"partialAccountNumber can't be\ |
| | \ null\");\n boolean isEven = true;\n int total = 0;\n Long\ |
| | \ temp = partialAccountNumber;\n\n while (temp > 0) {\n long\ |
| | \ digit = temp % 10;\n if (isEven) {\n long multipliedDigit\ |
| | \ = digit * 2;\n total += isTwoDigit(multipliedDigit) ? sumUpDigits(multipliedDigit)\ |
| | \ : multipliedDigit;\n } else {\n total += digit;\n\ |
| | \ }\n temp /= 10;\n isEven = !isEven;\n \ |
| | \ }\n\n int check = total * 9 % 10;\n\n return partialAccountNumber\ |
| | \ * 10 + check;\n }" |
| | - "func (d *Driver) DiffSize(id string, idMappings *idtools.IDMappings, parent string,\ |
| | \ parentMappings *idtools.IDMappings, mountLabel string) (size int64, err error)\ |
| | \ {\n\tif d.useNaiveDiff() || !d.isParent(id, parent) {\n\t\treturn d.naiveDiff.DiffSize(id,\ |
| | \ idMappings, parent, parentMappings, mountLabel)\n\t}\n\treturn directory.Size(d.getDiffPath(id))\n\ |
| | }" |
| | pipeline_tag: sentence-similarity |
| | library_name: sentence-transformers |
| | --- |
| | |
| | # SentenceTransformer based on Shuu12121/CodeModernBERT-Owl-4.1 |
| |
|
| | This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Shuu12121/CodeModernBERT-Owl-4.1](https://huggingface.co/Shuu12121/CodeModernBERT-Owl-4.1). 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-4.1](https://huggingface.co/Shuu12121/CodeModernBERT-Owl-4.1) <!-- at revision 1daade193254e92a8593c9fe97fc80e2cb742df4 --> |
| | - **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 = [ |
| | 'Register the router instance.\n\n@return void', |
| | "protected function registerRouter()\n\t{\n\t\t$this->app['router'] = $this->app->share(function($app)\n\t\t{\n\t\t\treturn new Router($app['events'], $app);\n\t\t});\n\t}", |
| | 'func (d *Driver) DiffSize(id string, idMappings *idtools.IDMappings, parent string, parentMappings *idtools.IDMappings, mountLabel string) (size int64, err error) {\n\tif d.useNaiveDiff() || !d.isParent(id, parent) {\n\t\treturn d.naiveDiff.DiffSize(id, idMappings, parent, parentMappings, mountLabel)\n\t}\n\treturn directory.Size(d.getDiffPath(id))\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: 6,960,000 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: 50.31 tokens</li><li>max: 1024 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 164.73 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>// GetNodeID returns the NodeID field if it's non-nil, zero value otherwise.</code> | <code>func (a *App) GetNodeID() string {<br> if a == nil || a.NodeID == nil {<br> return ""<br> }<br> return *a.NodeID<br>}</code> | <code>1.0</code> | |
| | | <code>// SignVote signs a canonical representation of the vote, along with the<br>// chainID. Implements PrivValidator.</code> | <code>func (pv *FilePV) SignVote(chainID string, vote *types.Vote) error {<br> if err := pv.signVote(chainID, vote); err != nil {<br> return fmt.Errorf("error signing vote: %v", err)<br> }<br> return nil<br>}</code> | <code>1.0</code> | |
| | | <code>//GetQyAccessToken 获取access_token</code> | <code>func (ctx *Context) GetQyAccessToken() (accessToken string, err error) {<br> ctx.accessTokenLock.Lock()<br> defer ctx.accessTokenLock.Unlock()<br><br> accessTokenCacheKey := fmt.Sprintf("qy_access_token_%s", ctx.AppID)<br> val := ctx.Cache.Get(accessTokenCacheKey)<br> if val != nil {<br> accessToken = val.(string)<br> return<br> }<br><br> //从微信服务器获取<br> var resQyAccessToken ResQyAccessToken<br> resQyAccessToken, err = ctx.GetQyAccessTokenFromServer()<br> if err != nil {<br> return<br> }<br><br> accessToken = resQyAccessToken.AccessToken<br> return<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`: 250 |
| | - `per_device_eval_batch_size`: 250 |
| | - `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`: 250 |
| | - `per_device_eval_batch_size`: 250 |
| | - `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 |
| | - `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`: False |
| | - `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`: 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 | |
| | |:------:|:-----:|:-------------:| |
| | | 0.0180 | 500 | 0.9746 | |
| | | 0.0359 | 1000 | 0.1636 | |
| | | 0.0539 | 1500 | 0.1502 | |
| | | 0.0718 | 2000 | 0.1374 | |
| | | 0.0898 | 2500 | 0.1314 | |
| | | 0.1078 | 3000 | 0.1241 | |
| | | 0.1257 | 3500 | 0.1152 | |
| | | 0.1437 | 4000 | 0.1146 | |
| | | 0.1616 | 4500 | 0.1065 | |
| | | 0.1796 | 5000 | 0.1014 | |
| | | 0.1976 | 5500 | 0.0983 | |
| | | 0.2155 | 6000 | 0.0987 | |
| | | 0.2335 | 6500 | 0.0917 | |
| | | 0.2514 | 7000 | 0.0912 | |
| | | 0.2694 | 7500 | 0.0896 | |
| | | 0.2874 | 8000 | 0.086 | |
| | | 0.3053 | 8500 | 0.0811 | |
| | | 0.3233 | 9000 | 0.0813 | |
| | | 0.3412 | 9500 | 0.082 | |
| | | 0.3592 | 10000 | 0.0759 | |
| | | 0.3772 | 10500 | 0.0753 | |
| | | 0.3951 | 11000 | 0.0722 | |
| | | 0.4131 | 11500 | 0.0707 | |
| | | 0.4310 | 12000 | 0.0699 | |
| | | 0.4490 | 12500 | 0.0698 | |
| | | 0.4670 | 13000 | 0.0679 | |
| | | 0.4849 | 13500 | 0.0653 | |
| | | 0.5029 | 14000 | 0.0641 | |
| | | 0.5208 | 14500 | 0.063 | |
| | | 0.5388 | 15000 | 0.0621 | |
| | | 0.5568 | 15500 | 0.061 | |
| | | 0.5747 | 16000 | 0.0581 | |
| | | 0.5927 | 16500 | 0.0555 | |
| | | 0.6106 | 17000 | 0.0552 | |
| | | 0.6286 | 17500 | 0.0551 | |
| | | 0.6466 | 18000 | 0.0533 | |
| | | 0.6645 | 18500 | 0.0521 | |
| | | 0.6825 | 19000 | 0.051 | |
| | | 0.7004 | 19500 | 0.0509 | |
| | | 0.7184 | 20000 | 0.0499 | |
| | | 0.7364 | 20500 | 0.0468 | |
| | | 0.7543 | 21000 | 0.0484 | |
| | | 0.7723 | 21500 | 0.0466 | |
| | | 0.7902 | 22000 | 0.0446 | |
| | | 0.8082 | 22500 | 0.0453 | |
| | | 0.8261 | 23000 | 0.0442 | |
| | | 0.8441 | 23500 | 0.0424 | |
| | | 0.8621 | 24000 | 0.0434 | |
| | | 0.8800 | 24500 | 0.0416 | |
| | | 0.8980 | 25000 | 0.0406 | |
| | | 0.9159 | 25500 | 0.0404 | |
| | | 0.9339 | 26000 | 0.0398 | |
| | | 0.9519 | 26500 | 0.0406 | |
| | | 0.9698 | 27000 | 0.0387 | |
| | | 0.9878 | 27500 | 0.0386 | |
| | | 1.0057 | 28000 | 0.0311 | |
| | | 1.0237 | 28500 | 0.0193 | |
| | | 1.0417 | 29000 | 0.0197 | |
| | | 1.0596 | 29500 | 0.0186 | |
| | | 1.0776 | 30000 | 0.0192 | |
| | | 1.0955 | 30500 | 0.0194 | |
| | | 1.1135 | 31000 | 0.0196 | |
| | | 1.1315 | 31500 | 0.0198 | |
| | | 1.1494 | 32000 | 0.0203 | |
| | | 1.1674 | 32500 | 0.02 | |
| | | 1.1853 | 33000 | 0.0184 | |
| | | 1.2033 | 33500 | 0.0181 | |
| | | 1.2213 | 34000 | 0.0195 | |
| | | 1.2392 | 34500 | 0.0186 | |
| | | 1.2572 | 35000 | 0.0184 | |
| | | 1.2751 | 35500 | 0.0184 | |
| | | 1.2931 | 36000 | 0.0194 | |
| | | 1.3111 | 36500 | 0.0191 | |
| | | 1.3290 | 37000 | 0.0183 | |
| | | 1.3470 | 37500 | 0.0179 | |
| | | 1.3649 | 38000 | 0.0179 | |
| | | 1.3829 | 38500 | 0.0178 | |
| | | 1.4009 | 39000 | 0.018 | |
| | | 1.4188 | 39500 | 0.0182 | |
| | | 1.4368 | 40000 | 0.0188 | |
| | | 1.4547 | 40500 | 0.0172 | |
| | | 1.4727 | 41000 | 0.0169 | |
| | | 1.4907 | 41500 | 0.0173 | |
| | | 1.5086 | 42000 | 0.0166 | |
| | | 1.5266 | 42500 | 0.0157 | |
| | | 1.5445 | 43000 | 0.0168 | |
| | | 1.5625 | 43500 | 0.0158 | |
| | | 1.5805 | 44000 | 0.016 | |
| | | 1.5984 | 44500 | 0.0166 | |
| | | 1.6164 | 45000 | 0.0168 | |
| | | 1.6343 | 45500 | 0.0162 | |
| | | 1.6523 | 46000 | 0.0153 | |
| | | 1.6703 | 46500 | 0.0149 | |
| | | 1.6882 | 47000 | 0.0158 | |
| | | 1.7062 | 47500 | 0.0152 | |
| | | 1.7241 | 48000 | 0.0147 | |
| | | 1.7421 | 48500 | 0.0146 | |
| | | 1.7601 | 49000 | 0.0145 | |
| | | 1.7780 | 49500 | 0.0148 | |
| | | 1.7960 | 50000 | 0.015 | |
| | | 1.8139 | 50500 | 0.0145 | |
| | | 1.8319 | 51000 | 0.0142 | |
| | | 1.8499 | 51500 | 0.014 | |
| | | 1.8678 | 52000 | 0.0139 | |
| | | 1.8858 | 52500 | 0.0133 | |
| | | 1.9037 | 53000 | 0.0135 | |
| | | 1.9217 | 53500 | 0.0131 | |
| | | 1.9397 | 54000 | 0.0134 | |
| | | 1.9576 | 54500 | 0.013 | |
| | | 1.9756 | 55000 | 0.0132 | |
| | | 1.9935 | 55500 | 0.0122 | |
| | | 2.0115 | 56000 | 0.0089 | |
| | | 2.0295 | 56500 | 0.0061 | |
| | | 2.0474 | 57000 | 0.0061 | |
| | | 2.0654 | 57500 | 0.006 | |
| | | 2.0833 | 58000 | 0.0062 | |
| | | 2.1013 | 58500 | 0.0058 | |
| | | 2.1193 | 59000 | 0.0059 | |
| | | 2.1372 | 59500 | 0.0059 | |
| | | 2.1552 | 60000 | 0.0059 | |
| | | 2.1731 | 60500 | 0.0058 | |
| | | 2.1911 | 61000 | 0.0059 | |
| | | 2.2091 | 61500 | 0.0058 | |
| | | 2.2270 | 62000 | 0.0059 | |
| | | 2.2450 | 62500 | 0.0058 | |
| | | 2.2629 | 63000 | 0.0057 | |
| | | 2.2809 | 63500 | 0.0055 | |
| | | 2.2989 | 64000 | 0.0056 | |
| | | 2.3168 | 64500 | 0.0056 | |
| | | 2.3348 | 65000 | 0.0056 | |
| | | 2.3527 | 65500 | 0.0057 | |
| | | 2.3707 | 66000 | 0.0055 | |
| | | 2.3886 | 66500 | 0.0056 | |
| | | 2.4066 | 67000 | 0.0054 | |
| | | 2.4246 | 67500 | 0.0055 | |
| | | 2.4425 | 68000 | 0.0052 | |
| | | 2.4605 | 68500 | 0.0053 | |
| | | 2.4784 | 69000 | 0.0052 | |
| | | 2.4964 | 69500 | 0.0053 | |
| | | 2.5144 | 70000 | 0.0052 | |
| | | 2.5323 | 70500 | 0.0052 | |
| | | 2.5503 | 71000 | 0.0051 | |
| | | 2.5682 | 71500 | 0.0049 | |
| | | 2.5862 | 72000 | 0.005 | |
| | | 2.6042 | 72500 | 0.0047 | |
| | | 2.6221 | 73000 | 0.0048 | |
| | | 2.6401 | 73500 | 0.0047 | |
| | | 2.6580 | 74000 | 0.0048 | |
| | | 2.6760 | 74500 | 0.0048 | |
| | | 2.6940 | 75000 | 0.0048 | |
| | | 2.7119 | 75500 | 0.0047 | |
| | | 2.7299 | 76000 | 0.0047 | |
| | | 2.7478 | 76500 | 0.0046 | |
| | | 2.7658 | 77000 | 0.0046 | |
| | | 2.7838 | 77500 | 0.0044 | |
| | | 2.8017 | 78000 | 0.0046 | |
| | | 2.8197 | 78500 | 0.0047 | |
| | | 2.8376 | 79000 | 0.0045 | |
| | | 2.8556 | 79500 | 0.0043 | |
| | | 2.8736 | 80000 | 0.0045 | |
| | | 2.8915 | 80500 | 0.0044 | |
| | | 2.9095 | 81000 | 0.0045 | |
| | | 2.9274 | 81500 | 0.0045 | |
| | | 2.9454 | 82000 | 0.0043 | |
| | | 2.9634 | 82500 | 0.0042 | |
| | | 2.9813 | 83000 | 0.0041 | |
| | | 2.9993 | 83500 | 0.0044 | |
| | |
| | </details> |
| | |
| | ### Framework Versions |
| | - Python: 3.11.13 |
| | - Sentence Transformers: 4.1.0 |
| | - Transformers: 4.53.0 |
| | - PyTorch: 2.6.0+cu124 |
| | - Accelerate: 1.8.1 |
| | - Datasets: 3.6.0 |
| | - Tokenizers: 0.21.2 |
| | |
| | ## 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", |
| | } |
| | ``` |
| | |
| | #### 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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