| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:86807 |
| - loss:CosineSimilarityLoss |
| base_model: sentence-transformers/all-MiniLM-L6-v2 |
| widget: |
| - source_sentence: "[CLS] [KNOWLEDGE] NLTK [CTX] Programmierung einer pre-processing\ |
| \ CI/CD Pipeline zur automatisierten Verarbeitung von Newsartikeln von Tamil zu\ |
| \ Englisch, in Python unter Verwendung von NLTK und SpaCy\n [SEP]" |
| sentences: |
| - "[CLS] [KNOWLEDGE] SPOC [CTX] Single Point of Contact (SPOC) für die Business\ |
| \ Units (Schnittstellenfunktion zu anderen Teilprojekten und Teams)\n [SEP]" |
| - "[CLS] [KNOWLEDGE] DIN 50001 [CTX] Mitarbeit zur Einführung eines Energiemanagementsystems\ |
| \ nach DIN 50001\n [SEP]" |
| - "[CLS] [KNOWLEDGE] Risikomanagement [CTX] - Risikomanagement\n [SEP]" |
| - source_sentence: "[CLS] [KNOWLEDGE] 365 Tenant [CTX] Verantwortung Umzug Office\ |
| \ 365 Tenant\n [SEP]" |
| sentences: |
| - '[CLS] [KNOWLEDGE] Change- [CTX] Change-, Problem-, Incident- und Releasemanagement |
| mit IBM Maximo. [SEP]' |
| - "[CLS] [SKILL] Performance-Untersuchung Oracle-Datenbanken [CTX] Reorganisation\ |
| \ von SAP R/3 Systemen bzw. Oracle Datenbanken 10.2 mittels BRSPACE. Performance-Untersuchung\ |
| \ und Parametrisierung von SAP R/3 Systemen und Oracle-Datenbanken 10g. Incident-Bearbeitung\ |
| \ bzgl. SAP, Oracle und Unix über Remedy. Sap-Kernel\n [SEP]" |
| - '[CLS] [SKILL] Visualisierung DWH Mart Daten [CTX] Modellierung des Datenstroms |
| und erstellen von DWH Modellen mit Hilfe von ETL für Staging, Storage und Mart. |
| Visualisierung der DWH Mart Daten mit Microsoft Power BI. [SEP]' |
| - source_sentence: "[CLS] [KNOWLEDGE] Rechenzentrum [CTX] Abbau der Racks im alten\ |
| \ Rechenzentrum\n [SEP]" |
| sentences: |
| - "[CLS] [KNOWLEDGE] Rechenzentrum [CTX] Abbau der Racks im alten Rechenzentrum\n\ |
| \ [SEP]" |
| - "[CLS] [KNOWLEDGE] SAP [CTX] SAP Administration, Planung und Bereitstellung, Heterogene\ |
| \ und Homogene Systemkopien, Upgrade, Solution Manger, LVM Enterprise (post copy\ |
| \ automation), Patch OS/SAP/DB, config, SAP\n [SEP]" |
| - "[CLS] [SKILL] Erstellung Nutzerdatenbank [CTX] >- Erstellung einer Nutzerdatenbank\ |
| \ mit DynamoDB\n [SEP]" |
| - source_sentence: "[CLS] [KNOWLEDGE] EU-DSGVO [CTX] Datenschutzrecht (Datenschutz-Grundverordnung\ |
| \ (EU-DSGVO) und Bundesdatenschutzgesetz (BDSG neu)\n [SEP]" |
| sentences: |
| - "[CLS] [KNOWLEDGE] ITIL [CTX] Mitarbeit innerhalb der Prozesse der Prozesse Incident-Management\ |
| \ / Service-Desk, Problem-Management, Change-Management, Release-Management, Availability-Management\ |
| \ gem. ITIL\n [SEP]" |
| - "[CLS] [KNOWLEDGE] DSGVO [CTX] - Unterstützung und Beratung bei Architektur Fragen,\ |
| \ betreffend lfd. BSI- Standards DSGVO/ GDPR Themen (ISO- Normen & Standards)...\n\ |
| \ [SEP]" |
| - "[CLS] [KNOWLEDGE] RAS-Konfiguration [CTX] RAS-Konfiguration\n [SEP]" |
| - source_sentence: "[CLS] [KNOWLEDGE] Kunden-Konzernstandards [CTX] Konzeption, Erstellung\ |
| \ und Umsetzung der IT-Sicherheitsrichtlinien für die Freigabe von geheimen Daten\ |
| \ gemäß Kunden-Konzernstandards (ISO 27001, BSI-Grundschutz)\n [SEP]" |
| sentences: |
| - '[CLS] [KNOWLEDGE] Kundenberatung [CTX] Kundenberatung [SEP]' |
| - "[CLS] [KNOWLEDGE] Service-Katalog [CTX] Beauftragen der Hard- und Software, bzw.\ |
| \ Leistungserbringung gem. Service-Katalog\n [SEP]" |
| - '[CLS] [KNOWLEDGE] Datenbanksystemen [CTX] Portierung mittels make-tools(make,vmake,makesap,mapro) |
| und Assembler-Routinen, Test und Support von DB2, Oracle und MaxDB auf Linux Sles8 |
| und Sles9. Erstellung shared-libraries mit Sap Kernel 620 und 640 durch Einspielen |
| von Perforce-Sourcen. Test C und C++ Precompiler -Programme. Testen Datenbankfuntionalitäten |
| über SAP-CCMS und DB bzw. SAP-Laufzeitparameter (Shared Memory etc.). Installieren |
| und testen von diversen SAP Komponenten SAP R/3 4.6D, SAP Kernel 620 und 640 unter |
| Linux/zLinux 2.4 und 2.6 (64 Bit-Adressierung) mit verschiedenen Datenbanksystemen |
| als Database-Server (Oracle, DB2 und MaxDB). Testen SAP-Transaktionen mit CATT |
| (SAP computer aided test tool) und anpassen von Python und Perl Schnittstellen. |
| AIX 5.33, z/OS, SAP R/3 4.6D, WAS 6.20/6.40, DB2 V8.1, vi, K-Shell, C-Shell, bash, |
| ICLI, FTP, Smitty, TSO/ISPF, JCL, SAPSERV, AIX VG-PP-LP, yank, MaxDB 7.5, Linux/zLinux |
| 2.4/2.6, C, C++, Python, Perl Teamarbeit 3 Mitarbeiter. [SEP]' |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| metrics: |
| - pearson_cosine |
| - spearman_cosine |
| model-index: |
| - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 |
| results: |
| - task: |
| type: semantic-similarity |
| name: Semantic Similarity |
| dataset: |
| name: sts dev |
| type: sts-dev |
| metrics: |
| - type: pearson_cosine |
| value: 0.9898120947090514 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.9570957645982657 |
| name: Spearman Cosine |
| - task: |
| type: semantic-similarity |
| name: Semantic Similarity |
| dataset: |
| name: sts test |
| type: sts-test |
| metrics: |
| - type: pearson_cosine |
| value: 0.9896747270285828 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.9591977829092115 |
| name: Spearman Cosine |
| --- |
| |
| # SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
|
|
| ## Model Details |
|
|
| ### Model Description |
| - **Model Type:** Sentence Transformer |
| - **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf --> |
| - **Maximum Sequence Length:** 256 tokens |
| - **Output Dimensionality:** 384 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': 256, 'do_lower_case': False}) with Transformer model: BertModel |
| (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
| (2): Normalize() |
| ) |
| ``` |
|
|
| ## Usage |
|
|
| ### Direct Usage (Sentence Transformers) |
|
|
| First install the Sentence Transformers library: |
|
|
| ```bash |
| pip install -U sentence-transformers |
| ``` |
|
|
| Then you can load this model and run inference. |
| ```python |
| from sentence_transformers import SentenceTransformer |
| |
| # Download from the 🤗 Hub |
| model = SentenceTransformer("deedcon/bi-encoder-v2") |
| # Run inference |
| sentences = [ |
| '[CLS] [KNOWLEDGE] Kunden-Konzernstandards [CTX] Konzeption, Erstellung und Umsetzung der IT-Sicherheitsrichtlinien für die Freigabe von geheimen Daten gemäß Kunden-Konzernstandards (ISO 27001, BSI-Grundschutz)\n [SEP]', |
| '[CLS] [KNOWLEDGE] Kundenberatung [CTX] Kundenberatung [SEP]', |
| '[CLS] [KNOWLEDGE] Service-Katalog [CTX] Beauftragen der Hard- und Software, bzw. Leistungserbringung gem. Service-Katalog\n [SEP]', |
| ] |
| embeddings = model.encode(sentences) |
| print(embeddings.shape) |
| # [3, 384] |
| |
| # 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.* |
| --> |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| #### Semantic Similarity |
|
|
| * Datasets: `sts-dev` and `sts-test` |
| * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
| | Metric | sts-dev | sts-test | |
| |:--------------------|:-----------|:-----------| |
| | pearson_cosine | 0.9898 | 0.9897 | |
| | **spearman_cosine** | **0.9571** | **0.9592** | |
| |
| <!-- |
| ## 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: 86,807 training samples |
| * Columns: <code>text1</code>, <code>text2</code>, and <code>score</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | text1 | text2 | score | |
| |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------| |
| | type | string | string | float | |
| | details | <ul><li>min: 15 tokens</li><li>mean: 62.0 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 61.69 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: -0.08</li><li>mean: 0.45</li><li>max: 1.0</li></ul> | |
| * Samples: |
| | text1 | text2 | score | |
| |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------| |
| | <code>[CLS] [SKILL] Dokumentieren Netzwerkinfrastruktur [CTX] Dokumentieren und Skizzieren der Netzwerkinfrastruktur. [SEP]</code> | <code>[CLS] [SKILL] Abarbeitung Incidents [CTX] Abarbeitung von Changes/ Incidents in Jira<br> [SEP]</code> | <code>0.1672067940235138</code> | |
| | <code>[CLS] [SKILL] Durchführung Abnahmetests [CTX] Erstellung von Testplänen und Durchführung von Abnahmetests im Rahmen des Release Managements. <br> [SEP]</code> | <code>[CLS] [KNOWLEDGE] Kostenrechnungswesen [CTX] Ist-Analyse der Produktionsabläufe sowie des vorhandene Kostenrechnungswesen . [SEP]</code> | <code>0.2705094516277313</code> | |
| | <code>[CLS] [KNOWLEDGE] MS SQL Datenbankabfragen [CTX] MS SQL Datenbankabfragen . [SEP]</code> | <code>[CLS] [SKILL] Erstellung MS SQL Datenbank [CTX] Erstellung & Modellierung der MS SQL Datenbank über Entity Framework<br> [SEP]</code> | <code>0.8471388816833496</code> | |
| * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: |
| ```json |
| { |
| "loss_fct": "torch.nn.modules.loss.MSELoss" |
| } |
| ``` |
| |
| ### Evaluation Dataset |
| |
| #### Unnamed Dataset |
| |
| * Size: 17,361 evaluation samples |
| * Columns: <code>text1</code>, <code>text2</code>, and <code>score</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | text1 | text2 | score | |
| |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------| |
| | type | string | string | float | |
| | details | <ul><li>min: 14 tokens</li><li>mean: 60.26 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 61.41 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: -0.09</li><li>mean: 0.46</li><li>max: 1.0</li></ul> | |
| * Samples: |
| | text1 | text2 | score | |
| |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------| |
| | <code>[CLS] [SKILL] Installation 7 [CTX] Installation, Konfiguration und Verwaltung von: Windows XP, 7, 8, 8.1, 10<br> [SEP]</code> | <code>[CLS] [SKILL] Installation 10 [CTX] Installation, Konfiguration und Verwaltung von: Windows XP, 7, 8, 8.1, 10<br> [SEP]</code> | <code>0.9105218082666396</code> | |
| | <code>[CLS] [KNOWLEDGE] Oracle-Systemadministration [CTX] Installation, Administration (Backup/Recovery etc.) und Tuning von DB2 nach SAP R/3 Gesichtspunkten bzw. DB2-Applikationsprogrammierung (Auswertung und Verarbeitung von DB2-Report-Utility und DB2-Katalog nach wiederherzustellenden SAP-Tablespaces mit recoverfähiger RBA zur Automatisierung des Conditional Restart Verfahrens) u. Oracle-Systemadministration in einer SAP R/3-Basis-Umgebung unter OS/390-TSO-ISPF-LIBRARIAN, DB2 Version 5 und DB2 UDB Version 6, CLIST, RACF, JCL, COBOL, AIX, ORACLE Version 7 u.8, Open-Edition, SAP R/3-BC 4.5B., Omegamon<br> [SEP]</code> | <code>[CLS] [KNOWLEDGE] RxJS [CTX] Konsumieren der Rest APIs mit HttpClient und RxJS<br> [SEP]</code> | <code>-0.06634289771318436</code> | |
| | <code>[CLS] [SKILL] Erstellung Prozessen [CTX] Erstellung von Dokumentationen und Arbeitsanweisungen und Prozessen Kommunikation auf allen Ebenen mit vielen Abteilungen international. [SEP]</code> | <code>[CLS] [KNOWLEDGE] DB2 8.1 [CTX] Systemadministration, Betrieb, Monitoring und Fehlerbehebung von über 100 SAP-Systemen unterschiedlicher Releasestände (4.6 C,6.20,6.40, EP, XI) und deren Datenbanken (Oracle 9.2, Informix 9.4, 9.3, DB2 8.1, SAP DB 7.5) auf pSeries Rechnern unter AIX 5.3 und z/OS. Durchführung und Überwachung des Transport-Managements (SAP-intern und auf OP-Ebene(AIX)) und<br> [SEP]</code> | <code>-0.009634226560592651</code> | |
| * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: |
| ```json |
| { |
| "loss_fct": "torch.nn.modules.loss.MSELoss" |
| } |
| ``` |
| |
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
|
|
| - `eval_strategy`: steps |
| - `per_device_train_batch_size`: 16 |
| - `per_device_eval_batch_size`: 16 |
| - `learning_rate`: 2e-05 |
| - `num_train_epochs`: 1 |
| - `warmup_ratio`: 0.1 |
| - `fp16`: True |
|
|
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
|
|
| - `overwrite_output_dir`: False |
| - `do_predict`: False |
| - `eval_strategy`: steps |
| - `prediction_loss_only`: True |
| - `per_device_train_batch_size`: 16 |
| - `per_device_eval_batch_size`: 16 |
| - `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`: 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`: {} |
| - `warmup_ratio`: 0.1 |
| - `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 |
| - `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 |
| - `dispatch_batches`: None |
| - `split_batches`: 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 |
| - `eval_use_gather_object`: False |
| - `average_tokens_across_devices`: False |
| - `prompts`: None |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: proportional |
|
|
| </details> |
|
|
| ### Training Logs |
| | Epoch | Step | Training Loss | Validation Loss | sts-dev_spearman_cosine | sts-test_spearman_cosine | |
| |:------:|:----:|:-------------:|:---------------:|:-----------------------:|:------------------------:| |
| | -1 | -1 | - | - | 0.8023 | - | |
| | 0.0921 | 500 | 0.0172 | 0.0056 | 0.9152 | - | |
| | 0.1843 | 1000 | 0.0064 | 0.0043 | 0.9330 | - | |
| | 0.2764 | 1500 | 0.005 | 0.0037 | 0.9399 | - | |
| | 0.3686 | 2000 | 0.0047 | 0.0035 | 0.9456 | - | |
| | 0.4607 | 2500 | 0.0042 | 0.0032 | 0.9475 | - | |
| | 0.5529 | 3000 | 0.0039 | 0.0029 | 0.9514 | - | |
| | 0.6450 | 3500 | 0.0036 | 0.0027 | 0.9525 | - | |
| | 0.7372 | 4000 | 0.0034 | 0.0027 | 0.9547 | - | |
| | 0.8293 | 4500 | 0.0033 | 0.0026 | 0.9564 | - | |
| | 0.9215 | 5000 | 0.0033 | 0.0025 | 0.9571 | - | |
| | -1 | -1 | - | - | - | 0.9592 | |
|
|
|
|
| ### Framework Versions |
| - Python: 3.12.4 |
| - Sentence Transformers: 4.1.0 |
| - Transformers: 4.49.0 |
| - PyTorch: 2.4.0+rocm6.3.4.git7cecbf6d |
| - Accelerate: 1.6.0 |
| - Datasets: 3.5.0 |
| - Tokenizers: 0.21.1 |
|
|
| ## Citation |
|
|
| ### BibTeX |
|
|
| #### Sentence Transformers |
| ```bibtex |
| @inproceedings{reimers-2019-sentence-bert, |
| title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
| author = "Reimers, Nils and Gurevych, Iryna", |
| booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
| month = "11", |
| year = "2019", |
| publisher = "Association for Computational Linguistics", |
| url = "https://arxiv.org/abs/1908.10084", |
| } |
| ``` |
|
|
| <!-- |
| ## 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.* |
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| ## Model Card Contact |
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| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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