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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]
```
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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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### Out-of-Scope Use
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## 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",
}
```
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