--- 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) - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 384 dimensions - **Similarity Function:** Cosine Similarity ### 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] ``` ## Evaluation ### Metrics #### Semantic Similarity * Datasets: `sts-dev` and `sts-test` * Evaluated with [EmbeddingSimilarityEvaluator](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** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 86,807 training samples * Columns: text1, text2, and score * Approximate statistics based on the first 1000 samples: | | text1 | text2 | score | |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | text1 | text2 | score | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------| | [CLS] [SKILL] Dokumentieren Netzwerkinfrastruktur [CTX] Dokumentieren und Skizzieren der Netzwerkinfrastruktur. [SEP] | [CLS] [SKILL] Abarbeitung Incidents [CTX] Abarbeitung von Changes/ Incidents in Jira
[SEP] | 0.1672067940235138 | | [CLS] [SKILL] Durchführung Abnahmetests [CTX] Erstellung von Testplänen und Durchführung von Abnahmetests im Rahmen des Release Managements.
[SEP] | [CLS] [KNOWLEDGE] Kostenrechnungswesen [CTX] Ist-Analyse der Produktionsabläufe sowie des vorhandene Kostenrechnungswesen . [SEP] | 0.2705094516277313 | | [CLS] [KNOWLEDGE] MS SQL Datenbankabfragen [CTX] MS SQL Datenbankabfragen . [SEP] | [CLS] [SKILL] Erstellung MS SQL Datenbank [CTX] Erstellung & Modellierung der MS SQL Datenbank über Entity Framework
[SEP] | 0.8471388816833496 | * Loss: [CosineSimilarityLoss](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: text1, text2, and score * Approximate statistics based on the first 1000 samples: | | text1 | text2 | score | |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | text1 | text2 | score | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------| | [CLS] [SKILL] Installation 7 [CTX] Installation, Konfiguration und Verwaltung von: Windows XP, 7, 8, 8.1, 10
[SEP] | [CLS] [SKILL] Installation 10 [CTX] Installation, Konfiguration und Verwaltung von: Windows XP, 7, 8, 8.1, 10
[SEP] | 0.9105218082666396 | | [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
[SEP] | [CLS] [KNOWLEDGE] RxJS [CTX] Konsumieren der Rest APIs mit HttpClient und RxJS
[SEP] | -0.06634289771318436 | | [CLS] [SKILL] Erstellung Prozessen [CTX] Erstellung von Dokumentationen und Arbeitsanweisungen und Prozessen Kommunikation auf allen Ebenen mit vielen Abteilungen international. [SEP] | [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
[SEP] | -0.009634226560592651 | * Loss: [CosineSimilarityLoss](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
Click to expand - `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
### 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", } ```