| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - dense |
| - generated_from_trainer |
| - dataset_size:193623 |
| - loss:CachedMultipleNegativesRankingLoss |
| base_model: answerdotai/ModernBERT-base |
| widget: |
| - source_sentence: "@Override\n public void encode(final OtpOutputStream buf) {\n\ |
| \ final int arity = elems.length;\n\n buf.write_tuple_head(arity);\n\ |
| \n for (int i = 0; i < arity; i++) {\n buf.write_any(elems[i]);\n\ |
| \ }\n }" |
| sentences: |
| - fetch function with the same interface than in cozy-client-js |
| - 'Convert this tuple to the equivalent Erlang external representation. |
| |
| |
| @param buf |
| |
| an output stream to which the encoded tuple should be written.' |
| - 'Delete a customer by it''s id. |
| |
| |
| @param int $id The id |
| |
| |
| @return bool |
| |
| @throws \Throwable in case something went wrong when deleting.' |
| - source_sentence: "func (md *RootMetadata) KeyGenerationsToUpdate() (kbfsmd.KeyGen,\ |
| \ kbfsmd.KeyGen) {\n\treturn md.bareMd.KeyGenerationsToUpdate()\n}" |
| sentences: |
| - 'Return a mapping of table to alias for the primary table and joins. |
| |
| |
| @return array' |
| - // KeyGenerationsToUpdate wraps the respective method of the underlying BareRootMetadata |
| for convenience. |
| - " Platform.valueOf(platformName);\n DesiredCapabilities desiredCapabilities\ |
| \ = new DesiredCapabilities(browser, version, platform);\n desiredCapabilities.setVersion(version);\n\ |
| \ return createAndSetRemoteDriver(url, desiredCapabilities);\n }" |
| - source_sentence: "func (f *fsClient) GetAccess() (access string, policyJSON string,\ |
| \ err *probe.Error) {\n\t// For windows this feature is not implemented.\n\tif\ |
| \ runtime.GOOS == \"windows\" {\n\t\treturn \"\", \"\", probe.NewError(APINotImplemented{API:\ |
| \ \"GetAccess\", APIType: \"filesystem\"})\n\t}\n\tst, err := f.fsStat(false)\n\ |
| \tif err != nil {\n" |
| sentences: |
| - "\t\treturn \"\", \"\", err.Trace(f.PathURL.String())\n\t}\n\tif !st.Mode().IsDir()\ |
| \ {\n\t\treturn \"\", \"\", probe.NewError(APINotImplemented{API: \"GetAccess\"\ |
| , APIType: \"filesystem\"})\n\t}\n\t// Mask with os.ModePerm to get only inode\ |
| \ permissions\n\tswitch st.Mode() & os.ModePerm {\n\tcase os.FileMode(0777):\n\ |
| \t\treturn \"readwrite\", \"\", nil\n\tcase os.FileMode(0555):\n\t\treturn \"\ |
| readonly\", \"\", nil\n\tcase os.FileMode(0333):\n\t\treturn \"writeonly\", \"\ |
| \", nil\n\t}\n\treturn \"none\", \"\", nil\n}" |
| - // DeleteOperator deletes the specified operator. |
| - " foreach ($files as $storedfile) {\n $fs->import_external_file($storedfile);\n\ |
| \ }\n }" |
| - source_sentence: "def close_database_session(session):\n \"\"\"Close connection\ |
| \ with the database\"\"\"\n\n try:\n session.close()\n except OperationalError\ |
| \ as e:\n raise DatabaseError(error=e.orig.args[1], code=e.orig.args[0])" |
| sentences: |
| - " if (is_array($this->data)) {\n $this->data[$attributeKey]\ |
| \ = is_callable($attributeValue) ? $attributeValue($this->rawData) : $attributeValue;\n\ |
| \ } else {\n $this->data->$attributeKey = is_callable($attributeValue)\ |
| \ ? $attributeValue($this->rawData) : $attributeValue;\n }\n \ |
| \ }\n return $this;\n }\n\n if (is_array($this->data))\ |
| \ {\n $this->data[$name] = is_callable($value) ? $value($this->rawData)\ |
| \ : $value;\n } else {\n $this->data->$name = is_callable($value)\ |
| \ ? $value($this->rawData) : $value;\n }\n\n return $this;\n \ |
| \ }" |
| - 'Waits for the timeout duration until the url responds with correct status code |
| |
| |
| @param routeUrl URL to check (usually a route one) |
| |
| @param timeout Max timeout value to await for route readiness. |
| |
| If not set, default timeout value is set to 5. |
| |
| @param timeoutUnit TimeUnit used for timeout duration. |
| |
| If not set, Minutes is used as default TimeUnit. |
| |
| @param repetitions How many times in a row the route must respond successfully |
| to be considered available. |
| |
| @param statusCodes list of status code that might return that service is up and |
| running. |
| |
| It is used as OR, so if one returns true, then the route is considered valid. |
| |
| If not set, then only 200 status code is used.' |
| - Close connection with the database |
| - source_sentence: "function onActiveEditorChanged(event, current, previous) {\n \ |
| \ if (current && !current._codeMirror._lineFolds) {\n enableFoldingInEditor(current);\n\ |
| \ " |
| sentences: |
| - Get playback settings such as shuffle and repeat. |
| - 'Save config data. |
| |
| |
| @param string $path |
| |
| @param string $value |
| |
| @param string $scope |
| |
| @param int $scopeId |
| |
| |
| @return null' |
| - " }\n if (previous) {\n saveLineFolds(previous);\n \ |
| \ }\n }" |
| datasets: |
| - benjamintli/code-retrieval-combined |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| metrics: |
| - cosine_accuracy@1 |
| - cosine_accuracy@3 |
| - 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@10 |
| - cosine_map@100 |
| model-index: |
| - name: SentenceTransformer based on answerdotai/ModernBERT-base |
| results: |
| - task: |
| type: information-retrieval |
| name: Information Retrieval |
| dataset: |
| name: eval |
| type: eval |
| metrics: |
| - type: cosine_accuracy@1 |
| value: 0.9167054011341452 |
| name: Cosine Accuracy@1 |
| - type: cosine_accuracy@3 |
| value: 0.9643023147717765 |
| name: Cosine Accuracy@3 |
| - type: cosine_accuracy@5 |
| value: 0.9737845124105233 |
| name: Cosine Accuracy@5 |
| - type: cosine_accuracy@10 |
| value: 0.9822441201078368 |
| name: Cosine Accuracy@10 |
| - type: cosine_precision@1 |
| value: 0.9167054011341452 |
| name: Cosine Precision@1 |
| - type: cosine_precision@3 |
| value: 0.32143410492392543 |
| name: Cosine Precision@3 |
| - type: cosine_precision@5 |
| value: 0.19475690248210473 |
| name: Cosine Precision@5 |
| - type: cosine_precision@10 |
| value: 0.09822441201078369 |
| name: Cosine Precision@10 |
| - type: cosine_recall@1 |
| value: 0.9167054011341452 |
| name: Cosine Recall@1 |
| - type: cosine_recall@3 |
| value: 0.9643023147717765 |
| name: Cosine Recall@3 |
| - type: cosine_recall@5 |
| value: 0.9737845124105233 |
| name: Cosine Recall@5 |
| - type: cosine_recall@10 |
| value: 0.9822441201078368 |
| name: Cosine Recall@10 |
| - type: cosine_ndcg@10 |
| value: 0.9519116805931805 |
| name: Cosine Ndcg@10 |
| - type: cosine_mrr@10 |
| value: 0.9419304852801657 |
| name: Cosine Mrr@10 |
| - type: cosine_map@100 |
| value: 0.9425514042279245 |
| name: Cosine Map@100 |
| --- |
| |
| # SentenceTransformer based on answerdotai/ModernBERT-base |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the [code-retrieval-combined](https://huggingface.co/datasets/benjamintli/code-retrieval-combined) dataset. 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:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) <!-- at revision 8949b909ec900327062f0ebf497f51aef5e6f0c8 --> |
| - **Maximum Sequence Length:** 1024 tokens |
| - **Output Dimensionality:** 768 dimensions |
| - **Similarity Function:** Cosine Similarity |
| - **Training Dataset:** |
| - [code-retrieval-combined](https://huggingface.co/datasets/benjamintli/code-retrieval-combined) |
| <!-- - **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({'max_seq_length': 1024, 'do_lower_case': False, 'architecture': 'OptimizedModule'}) |
| (1): Pooling({'word_embedding_dimension': 768, '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}) |
| ) |
| ``` |
|
|
| ## 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("modernbert-code") |
| # Run inference |
| queries = [ |
| "function onActiveEditorChanged(event, current, previous) {\n if (current \u0026\u0026 !current._codeMirror._lineFolds) {\n enableFoldingInEditor(current);\n ", |
| ] |
| documents = [ |
| ' }\n if (previous) {\n saveLineFolds(previous);\n }\n }', |
| 'Save config data.\n\n@param string $path\n@param string $value\n@param string $scope\n@param int $scopeId\n\n@return null', |
| 'Get playback settings such as shuffle and repeat.', |
| ] |
| query_embeddings = model.encode_query(queries) |
| document_embeddings = model.encode_document(documents) |
| print(query_embeddings.shape, document_embeddings.shape) |
| # [1, 768] [3, 768] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(query_embeddings, document_embeddings) |
| print(similarities) |
| # tensor([[0.6443, 0.0381, 0.0291]]) |
| ``` |
|
|
| <!-- |
| ### 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: `eval` |
| * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
| | Metric | Value | |
| |:--------------------|:-----------| |
| | cosine_accuracy@1 | 0.9167 | |
| | cosine_accuracy@3 | 0.9643 | |
| | cosine_accuracy@5 | 0.9738 | |
| | cosine_accuracy@10 | 0.9822 | |
| | cosine_precision@1 | 0.9167 | |
| | cosine_precision@3 | 0.3214 | |
| | cosine_precision@5 | 0.1948 | |
| | cosine_precision@10 | 0.0982 | |
| | cosine_recall@1 | 0.9167 | |
| | cosine_recall@3 | 0.9643 | |
| | cosine_recall@5 | 0.9738 | |
| | cosine_recall@10 | 0.9822 | |
| | **cosine_ndcg@10** | **0.9519** | |
| | cosine_mrr@10 | 0.9419 | |
| | cosine_map@100 | 0.9426 | |
| |
| <!-- |
| ## 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 |
| |
| #### code-retrieval-combined |
| |
| * Dataset: [code-retrieval-combined](https://huggingface.co/datasets/benjamintli/code-retrieval-combined) at [4403b52](https://huggingface.co/datasets/benjamintli/code-retrieval-combined/tree/4403b525f5962df8374b128e0863482e07cb1dc9) |
| * Size: 193,623 training samples |
| * Columns: <code>query</code> and <code>positive</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | query | positive | |
| |:--------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
| | type | string | string | |
| | details | <ul><li>min: 6 tokens</li><li>mean: 143.24 tokens</li><li>max: 1024 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 64.75 tokens</li><li>max: 937 tokens</li></ul> | |
| * Samples: |
| | query | positive | |
| |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | <code>protected function sendMusicMsgToJsonString(WxSendMusicMsg $msg)<br> {<br> $formatStr = '{<br> "touser":"%s",<br> "msgtype":"%s",<br> "music":<br> {<br> "title":"%s",<br> "description":"%s",<br> "musicurl":"%s",<br> "hqmusicurl":"%s",<br> "thumb_media_id":"%s"<br> }<br> }';<br> $result = sprintf($formatStr, $msg->getToUserName(),<br> $msg->getMsgType(),<br> $msg->getTitle(),<br> $msg->getDescription(),<br> $msg->getMusicUrl(),<br> $msg->getHQMusicUrl(),<br> $msg->getThumbMediaId()<br> );<br><br> return $result;<br> }</code> | <code>formatter WxSendMusicMsg to Json string<br>@param WxSendMusicMsg $msg<br>@return string</code> | |
| | <code>def getBlocks(self):<br> """<br> Get the blocks that need to be migrated<br> """<br> try:<br> conn = self.dbi.connection()<br> result =</code> | <code> self.buflistblks.execute(conn)<br> return result<br> finally:<br> if conn:<br> conn.close()</code> | |
| | <code>function obj(/*key,value, key,value ...*/) {<br> var result = {}<br> for(var n=0; n<arguments.length; n+=2) {<br> result[arguments[n]] = arguments[n+1]<br> }<br> return result<br>}</code> | <code>builds an object immediate where keys can be expressions</code> | |
| * Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim", |
| "mini_batch_size": 128, |
| "gather_across_devices": false, |
| "directions": [ |
| "query_to_doc" |
| ], |
| "partition_mode": "joint", |
| "hardness_mode": null, |
| "hardness_strength": 0.0 |
| } |
| ``` |
| |
| ### Evaluation Dataset |
| |
| #### code-retrieval-combined |
| |
| * Dataset: [code-retrieval-combined](https://huggingface.co/datasets/benjamintli/code-retrieval-combined) at [4403b52](https://huggingface.co/datasets/benjamintli/code-retrieval-combined/tree/4403b525f5962df8374b128e0863482e07cb1dc9) |
| * Size: 21,514 evaluation samples |
| * Columns: <code>query</code> and <code>positive</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | query | positive | |
| |:--------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| |
| | type | string | string | |
| | details | <ul><li>min: 7 tokens</li><li>mean: 140.91 tokens</li><li>max: 1024 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 71.36 tokens</li><li>max: 1024 tokens</li></ul> | |
| * Samples: |
| | query | positive | |
| |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | <code>def save<br> self.attributes.stringify_keys!<br> self.attributes.delete('customer')<br> self.attributes.delete('product')<br> self.attributes.delete('credit_card')<br> self.attributes.delete('bank_account')<br> self.attributes.delete('paypal_account')<br><br> </code> | <code> self.attributes, options = extract_uniqueness_token(attributes)<br> self.prefix_options.merge!(options)<br> super<br> end</code> | |
| | <code>def _update_summary(self, summary=None):<br> """Update all parts of the summary or clear when no summary."""<br> board_image_label = self._parts['board image label']<br> # get content for update or use blanks when no summary<br> if summary:<br> # make a board image with the swap drawn on it<br> # board, action, text = summary.board, summary.action, summary.text<br> board_image_cv = self._create_board_image_cv(summary.board)<br> self._draw_swap_cv(board_image_cv, summary.action)<br> board_image_tk = self._convert_cv_to_tk(board_image_cv)<br> text = ''<br> if not summary.score is None:<br> text += 'Score: {:3.1f}'.format(summary.score)<br> if (not summary.mana_drain_leaves is None) and\<br> (not summary.total_leaves is None):<br> text += ' Mana Drains: {}/{}' \<br> ''.format(summary.mana_drain_leaves,<br> </code> | <code> summary.total_leaves)<br> else:<br> #clear any stored state image and use the blank<br> board_image_tk = board_image_label._blank_image<br> text = ''<br> # update the UI parts with the content<br> board_image_label._board_image = board_image_tk<br> board_image_label.config(image=board_image_tk)<br> # update the summary text<br> summary_label = self._parts['summary label']<br> summary_label.config(text=text)<br> # refresh the UI<br> self._base.update()</code> | |
| | <code>def chi_p(mass1, mass2, spin1x, spin1y, spin2x, spin2y):<br> """Returns the effective precession spin from mass1, mass2, spin1x,<br> spin1y, spin2x, and spin2y.<br> """<br> xi1 = secondary_xi(mass1, mass2, spin1x, spin1y, spin2x, spin2y)<br> xi2 = primary_xi(mass1, mass2, spin1x, spin1y, spin2x, spin2y)<br> return chi_p_from_xi1_xi2(xi1, xi2)</code> | <code>Returns the effective precession spin from mass1, mass2, spin1x,<br> spin1y, spin2x, and spin2y.</code> | |
| * Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim", |
| "mini_batch_size": 128, |
| "gather_across_devices": false, |
| "directions": [ |
| "query_to_doc" |
| ], |
| "partition_mode": "joint", |
| "hardness_mode": null, |
| "hardness_strength": 0.0 |
| } |
| ``` |
| |
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
| |
| - `per_device_train_batch_size`: 1024 |
| - `num_train_epochs`: 1 |
| - `learning_rate`: 8e-05 |
| - `warmup_steps`: 0.05 |
| - `bf16`: True |
| - `eval_strategy`: steps |
| - `per_device_eval_batch_size`: 1024 |
| - `push_to_hub`: True |
| - `hub_model_id`: modernbert-code |
| - `load_best_model_at_end`: True |
| - `dataloader_num_workers`: 4 |
| - `batch_sampler`: no_duplicates |
| |
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
| |
| - `per_device_train_batch_size`: 1024 |
| - `num_train_epochs`: 1 |
| - `max_steps`: -1 |
| - `learning_rate`: 8e-05 |
| - `lr_scheduler_type`: linear |
| - `lr_scheduler_kwargs`: None |
| - `warmup_steps`: 0.05 |
| - `optim`: adamw_torch_fused |
| - `optim_args`: None |
| - `weight_decay`: 0.0 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.999 |
| - `adam_epsilon`: 1e-08 |
| - `optim_target_modules`: None |
| - `gradient_accumulation_steps`: 1 |
| - `average_tokens_across_devices`: True |
| - `max_grad_norm`: 1.0 |
| - `label_smoothing_factor`: 0.0 |
| - `bf16`: True |
| - `fp16`: False |
| - `bf16_full_eval`: False |
| - `fp16_full_eval`: False |
| - `tf32`: None |
| - `gradient_checkpointing`: False |
| - `gradient_checkpointing_kwargs`: None |
| - `torch_compile`: False |
| - `torch_compile_backend`: None |
| - `torch_compile_mode`: None |
| - `use_liger_kernel`: False |
| - `liger_kernel_config`: None |
| - `use_cache`: False |
| - `neftune_noise_alpha`: None |
| - `torch_empty_cache_steps`: None |
| - `auto_find_batch_size`: False |
| - `log_on_each_node`: True |
| - `logging_nan_inf_filter`: True |
| - `include_num_input_tokens_seen`: no |
| - `log_level`: passive |
| - `log_level_replica`: warning |
| - `disable_tqdm`: False |
| - `project`: huggingface |
| - `trackio_space_id`: trackio |
| - `eval_strategy`: steps |
| - `per_device_eval_batch_size`: 1024 |
| - `prediction_loss_only`: True |
| - `eval_on_start`: False |
| - `eval_do_concat_batches`: True |
| - `eval_use_gather_object`: False |
| - `eval_accumulation_steps`: None |
| - `include_for_metrics`: [] |
| - `batch_eval_metrics`: False |
| - `save_only_model`: False |
| - `save_on_each_node`: False |
| - `enable_jit_checkpoint`: False |
| - `push_to_hub`: True |
| - `hub_private_repo`: None |
| - `hub_model_id`: modernbert-code |
| - `hub_strategy`: every_save |
| - `hub_always_push`: False |
| - `hub_revision`: None |
| - `load_best_model_at_end`: True |
| - `ignore_data_skip`: False |
| - `restore_callback_states_from_checkpoint`: False |
| - `full_determinism`: False |
| - `seed`: 42 |
| - `data_seed`: None |
| - `use_cpu`: False |
| - `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 |
| - `dataloader_drop_last`: False |
| - `dataloader_num_workers`: 4 |
| - `dataloader_pin_memory`: True |
| - `dataloader_persistent_workers`: False |
| - `dataloader_prefetch_factor`: None |
| - `remove_unused_columns`: True |
| - `label_names`: None |
| - `train_sampling_strategy`: random |
| - `length_column_name`: length |
| - `ddp_find_unused_parameters`: None |
| - `ddp_bucket_cap_mb`: None |
| - `ddp_broadcast_buffers`: False |
| - `ddp_backend`: None |
| - `ddp_timeout`: 1800 |
| - `fsdp`: [] |
| - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
| - `deepspeed`: None |
| - `debug`: [] |
| - `skip_memory_metrics`: True |
| - `do_predict`: False |
| - `resume_from_checkpoint`: None |
| - `warmup_ratio`: None |
| - `local_rank`: -1 |
| - `prompts`: None |
| - `batch_sampler`: no_duplicates |
| - `multi_dataset_batch_sampler`: proportional |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
| |
| </details> |
| |
| ### Training Logs |
| | Epoch | Step | Training Loss | Validation Loss | eval_cosine_ndcg@10 | |
| |:-------:|:-------:|:-------------:|:---------------:|:-------------------:| |
| | 0.0526 | 10 | 5.2457 | 2.4469 | 0.4195 | |
| | 0.1053 | 20 | 1.3973 | 0.6956 | 0.7742 | |
| | 0.1579 | 30 | 0.5500 | 0.4000 | 0.8560 | |
| | 0.2105 | 40 | 0.3429 | 0.2878 | 0.8891 | |
| | 0.2632 | 50 | 0.2487 | 0.2250 | 0.9104 | |
| | 0.3158 | 60 | 0.2080 | 0.1872 | 0.9256 | |
| | 0.3684 | 70 | 0.1768 | 0.1656 | 0.9312 | |
| | 0.4211 | 80 | 0.1525 | 0.1501 | 0.9352 | |
| | 0.4737 | 90 | 0.1402 | 0.1374 | 0.9397 | |
| | 0.5263 | 100 | 0.1343 | 0.1317 | 0.9413 | |
| | 0.5789 | 110 | 0.1217 | 0.1242 | 0.9444 | |
| | 0.6316 | 120 | 0.1180 | 0.1199 | 0.9454 | |
| | 0.6842 | 130 | 0.1164 | 0.1149 | 0.9476 | |
| | 0.7368 | 140 | 0.1146 | 0.1106 | 0.9494 | |
| | 0.7895 | 150 | 0.1091 | 0.1080 | 0.9494 | |
| | 0.8421 | 160 | 0.1085 | 0.1055 | 0.9506 | |
| | 0.8947 | 170 | 0.1062 | 0.1041 | 0.9511 | |
| | 0.9474 | 180 | 0.1130 | 0.1030 | 0.9517 | |
| | **1.0** | **190** | **0.0924** | **0.1024** | **0.9519** | |
|
|
| * The bold row denotes the saved checkpoint. |
|
|
| ### Framework Versions |
| - Python: 3.12.12 |
| - Sentence Transformers: 5.3.0 |
| - Transformers: 5.3.0 |
| - PyTorch: 2.10.0+cu128 |
| - Accelerate: 1.13.0 |
| - Datasets: 4.8.3 |
| - 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", |
| } |
| ``` |
|
|
| #### CachedMultipleNegativesRankingLoss |
| ```bibtex |
| @misc{gao2021scaling, |
| title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup}, |
| author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan}, |
| year={2021}, |
| eprint={2101.06983}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.LG} |
| } |
| ``` |
|
|
| <!-- |
| ## 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.* |
| --> |
