sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:25103 loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:1705.00652 autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:25103
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: PR4061978 OOL Action (TOC sur l'chantillon TANKF_SSS6_TOC prlev
    le 15 janvier 2024 par EMG
  sentences:
  - Vedolizumab Production Halted to Alarm Activation in Chromatography
  - Out-of-Limits Result for Tank F Sample TOC on January 18,
  - 'On 13Dec2022, during batch record review, Analyst EID 50320381) discovered that
    Fraction paste recovery below range for lot LR2249467 . Fraction IV-1 paste recovery
    was 46.39 g/kg of CPP which was below the range for 25% recovered lots (48.11
    to 61.04 g/kg of CPP) per FORM-050414 "Fr +III 25% Supernatant to Fr 1 PPT & Fr
    IV1 @ 25% supernatant" (Version 43.0, Effective Date: 01Nov2022). This deviation
    occurred in Building 5 Fractionation.'
- source_sentence: 'Instrument: Tolerable Error Limits for Balance R2105'
  sentences:
  - Metrology Out of tolerance of pipette Biohit Proline 100-200L of QC Lab, tag LEAE02416
  - LI PR4102547 -OoL Action mold) bio pour l'chantillon In Process SD du lot BE12E034Z
    prlev le 02 FEV 2024 -PL4
  - of range during the Fix/Display check on the LEWIT43108 in room R2105
- source_sentence: Lors de la rception de l'chantillon P104-DS10-LAL (lot Glassia
    BE22B017Z) au laboratoire QC de Takeda le Juin, ZONDACQ Antoine (QC Logistic Analyst,
    constat que le tube utilis pour le prlvement destin au testing LAL (SOP-048687
    n'tait pas tube valid . En effet, selon la procdure SOP-054100 "LE20LA02006B -
    Echantillonnage, identification, stockage et distribution des chantillons Glassia
    (Ligne 5)") ce sont des tubes "Falcon ref 3300446" qui doivent tre utiliss pour
    les prlvements destins au testing LAL (SOP-048687 . Or, l'chantillon impliqu par
    la prsente Dviation a t prlev dans un tube "Corning ref 430052" non adapt au prlvement
    d'chantillon LAL L'chantillon concern a t prlev le 08 Juin 2021 par KVDW (= date
    d'occurrence). Une deviation (event) est donc initie afin d'investiguer cette
    erreur de prlvement.
  sentences:
  - 'Lors de la completion de rendement dans le MBR du lot BE22B022Z (DS Glassia)
    le 02-Sep-2021, Cline Brunin (CBI, technicienne spcialiste EBM) a observ une valeur
    de rendement en alpha-1-antitrypsine (AAT) hors limites pour l''chantillon DS5
    (P102 - Aprs filtre presse) Valeur calcule: 107.1% Limite infrieure: 84.0% Limite
    suprieure 107.0%'
  - Out-of-Tolerance (OOT Calibration of HL-3170 Process Liquid UV Sensor at Los Angeles
    Manufacturing Facility
  - Use of a non-validated tube for the collection of sample P104-DS10-LAL of lot
    BE22B017Z
- source_sentence: NCR-000660 - 158-029 - Out of Tolerance
  sentences:
  - Etat du serveur Esxi dans VMware de Lessines aprs un problme d'adaptateur rse
  - 'Torque Wrench Asset ID #, owning department Purification, in room 1025, was NCR
    #for failure of calibration on (see Deviation 3066031 Attachment 1 NCR-000660).
    Previous Calibration was on 28Oct2021 with a calibration result of Pass . Review
    of Non-conformance History, including the deviation, resulted in 1 NCR (s) for
    this equipment from 4 events reviewed.'
  - LI PR 2928690 - OOL Alerte (cfu escalade en action sur l'chantillon WFI prlev
    le 08/AUG/2022
- source_sentence: Emergency Door in Staircase Room 1044 for Post-Viral Found Not
    Completely Closed
  sentences:
  - 'On 02Jul2023, Manufacturing Supervisor (EID 50251544) was informed that Post-Viral
    Exit Door in Grade C Staircase leading to uncontrolled space, was found opened
    . Additionally, on 04Jul2023, Manufacturing Supervisor (50251544) was that the
    same door in Room 1044 was found opened . Per TOOL-216083, "Global Job Aid, Takeda
    Glossary (Reference Only)" (Version, Effective Date: 20Jun2022, a deviation is
    a departure from an established process, system, procedure,, regulatory filing,
    Health Authority requirement, specification, tolerance, trend, or other conformance
    requirement that may have GXP impact . This deviation occurred in Building 5 Fractionation.'
  - Wrong autorization of packaging file for lot 20I25B437D
  - Deviation in DeltaV Recording During Wash Step of LA23G014 Elution Process
---

# SentenceTransformer

This model aims at encoding text information from deviations Titles and/or Deviation Description (Event) for various Takeda site.

This is a [sentence-transformers](https://www.SBERT.net) model trained. 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:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Emergency Door in Staircase Room 1044 for Post-Viral Found Not Completely Closed',
    'On 02Jul2023, Manufacturing Supervisor (EID 50251544) was informed that Post-Viral Exit Door in Grade C Staircase leading to uncontrolled space, was found opened . Additionally, on 04Jul2023, Manufacturing Supervisor (50251544) was that the same door in Room 1044 was found opened . Per TOOL-216083, "Global Job Aid, Takeda Glossary (Reference Only)" (Version, Effective Date: 20Jun2022, a deviation is a departure from an established process, system, procedure,, regulatory filing, Health Authority requirement, specification, tolerance, trend, or other conformance requirement that may have GXP impact . This deviation occurred in Building 5 Fractionation.',
    'Deviation in DeltaV Recording During Wash Step of LA23G014 Elution Process',
]
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]
```

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You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 25,103 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                         | sentence_1                                                                         |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             |
  | details | <ul><li>min: 4 tokens</li><li>mean: 66.28 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 72.37 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                                 | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
  |:-----------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>MFGR-0008591 Step 15.1/15.2 no</code>                                                                | <code>On 01NOV2022 at 2120 in room 1044, Manufacturing Associate ME1 discovered prompt for Connection to VP-5020 not appear at step 15.1 of MFGR-0008591 v1.0, VED-D, Capto Adhere Blank Chromatography Material 6254681, 12376356, Process Order 221191021 . Process Engineer NS was contacted and verified with Automation Engineer EDS that recipe does require prompt Connect to VP-5020 (step 15.1), Connect 5020 5011 (Step and Ready to Load into XX-XX (step 15.2). Quality CY and Quality Assurance Lead SSH were contacted gave approval to . On 02NOV2022 in room 1044, Manufacturing Associate ARF discovered prompt Connect Collection to VP-5231 did not appear at step 15.1 MFGR-0008592 v1.0, VED-D, Nuvia HR-S Blank Chromatography Material 6254682, 12376361, Process Order 221191023 . Manufacturing Supervisor D1A and Manufacturing Specialist JN were and instructed ARF to the prompt Connect Collection to VP-5231 and proceed with processing It was prompt to Load into XV-XX at step 15.2 also did not appear JN gave approval to proceed with processing.</code> |
  | <code>BE22D002Z - Ligne de transfert TP2110-TP2140 en statut sale expir</code>                             | <code>Ce dimanche 15/01/2023 18h50, Guillaume Deschuyteneer technicien Senior de production Glassia) a cr un work order EBM pour effectuer le CIP de dbut de de transfert line 2110-2140 (WO EBM: CIPG010048 pour la production du lot BE22D002Z . EBM alors spcifi Guillaume que le statut sanitaire de la line 2110-2140 tait en "sale expir". Guillaume a alors sa Cline Brunin (Contrematre de production Glassia) pour l'en informer.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
  | <code>Donne manquante initiale Glose l'chantillons SMA aprs capsulage du lot LE13X075 - LI PR215117</code> | <code>Initial Missing Data: agar observed on SMA after CAPPING batch LE13X075 - LI PR2151179</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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 50
- `multi_dataset_batch_sampler`: round_robin

#### 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`: 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`: 50
- `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`: False
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch  | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.3187 | 500  | 1.0372        |
| 0.6373 | 1000 | 0.3844        |
| 0.6667 | 1046 | -             |
| 0.9560 | 1500 | 0.2836        |
| 1.0    | 1569 | -             |
| 1.2747 | 2000 | 0.2401        |
| 1.3333 | 2092 | -             |
| 1.5934 | 2500 | 0.1983        |
| 1.9120 | 3000 | 0.1513        |
| 2.0    | 3138 | -             |
| 2.2307 | 3500 | 0.1278        |
| 2.5494 | 4000 | 0.1001        |
| 2.6667 | 4184 | -             |
| 2.8681 | 4500 | 0.0801        |
| 3.0    | 4707 | -             |
| 3.1867 | 5000 | 0.0707        |
| 3.3333 | 5230 | -             |
| 3.5054 | 5500 | 0.0479        |
| 3.8241 | 6000 | 0.0425        |
| 4.0    | 6276 | -             |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.0
- Transformers: 4.45.0.dev0
- PyTorch: 2.4.1
- Accelerate: 0.26.1
- Datasets: 2.16.1
- Tokenizers: 0.19.1

## 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",
}
```

#### 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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