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1_Pooling/config.json

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+{
+  "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
+}

README.md

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+---
+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 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]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## 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: 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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

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+{
+  "_name_or_path": "../../.ckpt/tsdae/",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.0.dev0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 105879
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.1.0",
+    "transformers": "4.45.0.dev0",
+    "pytorch": "2.4.1"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:30ead4a795a85f0f0131ee831102157f13e5ec901ff7b7031e56502ab4bd934e
+size 669448040

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}