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deviation_encoder_ALL

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:25103
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Inference Endpoints
Model card Files Community

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 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

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

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
    • min: 4 tokens
    • mean: 66.28 tokens
    • max: 512 tokens
    • min: 5 tokens
    • mean: 72.37 tokens
    • max: 512 tokens
  • 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 with these parameters:
    {
    "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 

@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 

@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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