--- 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 - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **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': 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] ``` ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 25,103 training samples * Columns: sentence_0 and sentence_1 * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence_0 | sentence_1 | |:-----------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | MFGR-0008591 Step 15.1/15.2 no | 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. | | BE22D002Z - Ligne de transfert TP2110-TP2140 en statut sale expir | 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. | | Donne manquante initiale Glose l'chantillons SMA aprs capsulage du lot LE13X075 - LI PR215117 | Initial Missing Data: agar observed on SMA after CAPPING batch LE13X075 - LI PR2151179 | * Loss: [MultipleNegativesRankingLoss](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
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`: 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
### 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} } ```