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--- |
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library_name: sentence-transformers |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:25103 |
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- loss:MultipleNegativesRankingLoss |
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widget: |
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- source_sentence: PR4061978 OOL Action (TOC sur l'chantillon TANKF_SSS6_TOC prlev |
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le 15 janvier 2024 par EMG |
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sentences: |
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- Vedolizumab Production Halted to Alarm Activation in Chromatography |
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- Out-of-Limits Result for Tank F Sample TOC on January 18, |
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- 'On 13Dec2022, during batch record review, Analyst EID 50320381) discovered that |
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Fraction paste recovery below range for lot LR2249467 . Fraction IV-1 paste recovery |
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was 46.39 g/kg of CPP which was below the range for 25% recovered lots (48.11 |
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to 61.04 g/kg of CPP) per FORM-050414 "Fr +III 25% Supernatant to Fr 1 PPT & Fr |
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IV1 @ 25% supernatant" (Version 43.0, Effective Date: 01Nov2022). This deviation |
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occurred in Building 5 Fractionation.' |
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- source_sentence: 'Instrument: Tolerable Error Limits for Balance R2105' |
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sentences: |
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- Metrology Out of tolerance of pipette Biohit Proline 100-200L of QC Lab, tag LEAE02416 |
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- LI PR4102547 -OoL Action mold) bio pour l'chantillon In Process SD du lot BE12E034Z |
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prlev le 02 FEV 2024 -PL4 |
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- of range during the Fix/Display check on the LEWIT43108 in room R2105 |
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- source_sentence: Lors de la rception de l'chantillon P104-DS10-LAL (lot Glassia |
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BE22B017Z) au laboratoire QC de Takeda le Juin, ZONDACQ Antoine (QC Logistic Analyst, |
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constat que le tube utilis pour le prlvement destin au testing LAL (SOP-048687 |
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n'tait pas tube valid . En effet, selon la procdure SOP-054100 "LE20LA02006B - |
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Echantillonnage, identification, stockage et distribution des chantillons Glassia |
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(Ligne 5)") ce sont des tubes "Falcon ref 3300446" qui doivent tre utiliss pour |
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les prlvements destins au testing LAL (SOP-048687 . Or, l'chantillon impliqu par |
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la prsente Dviation a t prlev dans un tube "Corning ref 430052" non adapt au prlvement |
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d'chantillon LAL L'chantillon concern a t prlev le 08 Juin 2021 par KVDW (= date |
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d'occurrence). Une deviation (event) est donc initie afin d'investiguer cette |
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erreur de prlvement. |
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sentences: |
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- 'Lors de la completion de rendement dans le MBR du lot BE22B022Z (DS Glassia) |
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le 02-Sep-2021, Cline Brunin (CBI, technicienne spcialiste EBM) a observ une valeur |
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de rendement en alpha-1-antitrypsine (AAT) hors limites pour l''chantillon DS5 |
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(P102 - Aprs filtre presse) Valeur calcule: 107.1% Limite infrieure: 84.0% Limite |
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suprieure 107.0%' |
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- Out-of-Tolerance (OOT Calibration of HL-3170 Process Liquid UV Sensor at Los Angeles |
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Manufacturing Facility |
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- Use of a non-validated tube for the collection of sample P104-DS10-LAL of lot |
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BE22B017Z |
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- source_sentence: NCR-000660 - 158-029 - Out of Tolerance |
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sentences: |
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- Etat du serveur Esxi dans VMware de Lessines aprs un problme d'adaptateur rse |
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- 'Torque Wrench Asset ID #, owning department Purification, in room 1025, was NCR |
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#for failure of calibration on (see Deviation 3066031 Attachment 1 NCR-000660). |
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Previous Calibration was on 28Oct2021 with a calibration result of Pass . Review |
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of Non-conformance History, including the deviation, resulted in 1 NCR (s) for |
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this equipment from 4 events reviewed.' |
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- LI PR 2928690 - OOL Alerte (cfu escalade en action sur l'chantillon WFI prlev |
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le 08/AUG/2022 |
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- source_sentence: Emergency Door in Staircase Room 1044 for Post-Viral Found Not |
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Completely Closed |
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sentences: |
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- 'On 02Jul2023, Manufacturing Supervisor (EID 50251544) was informed that Post-Viral |
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Exit Door in Grade C Staircase leading to uncontrolled space, was found opened |
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. Additionally, on 04Jul2023, Manufacturing Supervisor (50251544) was that the |
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same door in Room 1044 was found opened . Per TOOL-216083, "Global Job Aid, Takeda |
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Glossary (Reference Only)" (Version, Effective Date: 20Jun2022, a deviation is |
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a departure from an established process, system, procedure,, regulatory filing, |
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Health Authority requirement, specification, tolerance, trend, or other conformance |
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requirement that may have GXP impact . This deviation occurred in Building 5 Fractionation.' |
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- Wrong autorization of packaging file for lot 20I25B437D |
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- Deviation in DeltaV Recording During Wash Step of LA23G014 Elution Process |
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--- |
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# SentenceTransformer |
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This model aims at encoding text information from deviations Titles and/or Deviation Description (Event) for various Takeda site. |
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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. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 tokens |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel |
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(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}) |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("sentence_transformers_model_id") |
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# Run inference |
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sentences = [ |
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'Emergency Door in Staircase Room 1044 for Post-Viral Found Not Completely Closed', |
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'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.', |
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'Deviation in DeltaV Recording During Wash Step of LA23G014 Elution Process', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 25,103 training samples |
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* Columns: <code>sentence_0</code> and <code>sentence_1</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence_0 | sentence_1 | |
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|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
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| type | string | string | |
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| 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> | |
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* Samples: |
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| sentence_0 | sentence_1 | |
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|:-----------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <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> | |
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| <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> | |
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| <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> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `num_train_epochs`: 50 |
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- `multi_dataset_batch_sampler`: round_robin |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1 |
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- `num_train_epochs`: 50 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.0 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `use_liger_kernel`: False |
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- `eval_use_gather_object`: False |
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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: round_robin |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | |
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|:------:|:----:|:-------------:| |
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| 0.3187 | 500 | 1.0372 | |
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| 0.6373 | 1000 | 0.3844 | |
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| 0.6667 | 1046 | - | |
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| 0.9560 | 1500 | 0.2836 | |
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| 1.0 | 1569 | - | |
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| 1.2747 | 2000 | 0.2401 | |
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| 1.3333 | 2092 | - | |
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| 1.5934 | 2500 | 0.1983 | |
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| 1.9120 | 3000 | 0.1513 | |
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| 2.0 | 3138 | - | |
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| 2.2307 | 3500 | 0.1278 | |
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| 2.5494 | 4000 | 0.1001 | |
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| 2.6667 | 4184 | - | |
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| 2.8681 | 4500 | 0.0801 | |
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| 3.0 | 4707 | - | |
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| 3.1867 | 5000 | 0.0707 | |
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| 3.3333 | 5230 | - | |
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| 3.5054 | 5500 | 0.0479 | |
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| 3.8241 | 6000 | 0.0425 | |
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| 4.0 | 6276 | - | |
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### Framework Versions |
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- Python: 3.10.12 |
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- Sentence Transformers: 3.1.0 |
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- Transformers: 4.45.0.dev0 |
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- PyTorch: 2.4.1 |
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- Accelerate: 0.26.1 |
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- Datasets: 2.16.1 |
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- Tokenizers: 0.19.1 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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#### MultipleNegativesRankingLoss |
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```bibtex |
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@misc{henderson2017efficient, |
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title={Efficient Natural Language Response Suggestion for Smart Reply}, |
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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}, |
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year={2017}, |
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eprint={1705.00652}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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