Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "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

@@ -0,0 +1,969 @@
+---
+language:
+- en
+license: apache-2.0
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:17198
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: BAAI/bge-base-en-v1.5
+widget:
+- source_sentence: How should we proceed if the installed valve or its appurtenances
+    do not conform to the City's criteria, and what steps are involved in remedying
+    the situation?
+  sentences:
+  - '1.1 SUMMARY:
+
+    2. The valve shall perform as intended with no deformation, leaking or damage
+    of any kind for the pressure ranges indicated.
+
+    3. Before acceptance of the installed valve, provide the City the opportunity
+    to inspect and operate the valve.
+
+    a. The City will assess the ease of operating the ball valves and corporation
+    stops, where applicable.
+
+    4. The Combination Air Valve assembly shall be free from any leaks.
+
+    B. Non-Conforming Work
+
+    1. Ifaccess and operation of the valve or its appurtenances does not meet the
+    City''s criteria, the Contractor will remedy the situation until it meets the
+    City''s criteria, at the Contractor''s expense.
+
+    3.8. SYSTEM STARTUP [NOT USED]
+
+    3.9 ADJUSTING [NOT USED]
+
+    3.10 CLEANING [NOT USED]
+
+    3.11 CLOSEOUT ACTIVITIES [NOT USED]
+
+    3.12, PROTECTION [NOT USED]
+
+    3.13 MAINTENANCE [NOT USED]
+
+    3.14 ATTACHMENTS [NOT USED]'
+  - 'b. Miscellaneous Areas:
+
+    layer.. , 1 = . , 2 = 3). , 3 = Take corrective action if an adequate bond does
+    not exist between the. , 1 = . , 2 = . , 3 = current and underlying layer to ensure
+    an adequate bond will be achieved. , 1 = . , 2 = . , 3 = during subsequent placement
+    operations.. , 1 = . , 2 = 4). , 3 = The untrimmed core height must be in accordance
+    with the requirements in. , 1 = . , 2 = . , 3 = Table 17.. , 1 = . , 2 = 5). ,
+    3 = If the cores are an acceptable height, trim the cores immediately after. ,
+    1 = . , 2 = . , 3 = obtaining the cores in accordance with Tex-207-F.. , 1 = .
+    , 2 = . , 3 = Any core that does not meet the requirements in Table 17 will be
+    rejected.. , 1 = . , 2 = 6). , 3 = more. , 1 = . , 2 = . , 3 = The City may request
+    additional cores to be taken within the area. If. , 1 = . , 2 = 7). , 3 = than
+    2 cores are'
+  - '4. Other Activities:
+
+    a. Rental of storage units, rental of storage space for recreational vehicles
+    (RV) and boats, and the sale of moving related items are allowed uses.
+
+    b. No other land use or business activity within storage units is allowed.
+
+    c. The rental of moving trucks and moving related equipment shall constitute an
+    Equipment Sales and Rental use.
+
+    d. The sale and leasing of recreational vehicles (RVs) and boats shall constitute
+    an Automotive Sales and Leasing use.'
+- source_sentence: How do the setback regulations in the R1 District affect the design
+    and placement of structures on the lot?
+  sentences:
+  - 'B. R11 District Dimensional Standards:
+
+    Table 3.2-B: R1 District Dimensional Standards, 1 = Table 3.2-B: R1 District Dimensional
+    Standards. Table 3.2-B: R1 District Dimensional Standards, 2 = Table 3.2-B: R1
+    District Dimensional Standards. Table 3.2-B: R1 District Dimensional Standards,
+    3 = Table 3.2-B: R1 District Dimensional Standards. Dimensional Standards, 1 =
+    Dimensional Standards. Dimensional Standards, 2 = Dimensional Standards. Dimensional
+    Standards, 3 = Additional Standards. LOT DIMENSIONS (MINIMUM), 1 = LOT DIMENSIONS
+    (MINIMUM). LOT DIMENSIONS (MINIMUM), 2 = LOT DIMENSIONS (MINIMUM). LOT DIMENSIONS
+    (MINIMUM), 3 = LOT DIMENSIONS (MINIMUM). oN, 1 = Lot area. oN, 2 = 32,000 sq ft.
+    oN, 3 = 3.7.2: Lot and Site Requirements. iam, 1 = Lot width. iam, 2 = 80 feet.
+    iam, 3 = 3.7.2: Lot and Site Requirements. fem, 1 = Lot depth 100. fem, 2 = feet.
+    fem, 3 = 3.7.2: Lot and Site Requirements. SETBACKS'
+  - '1. R7 Zoning District:
+
+    Self-service laundry facilities shall only be permitted as an accessory use to
+    multifamily dwellings, and such use shall be located within a multifamily structure.'
+  - '1.3 > REFERENCES:
+
+    NY, 1 = A.. NY, 2 = Abbreviations and Acronyms 1. AWG: American wire gauge 2..
+    Fw, 1 = . Fw, 2 = PVC: polyvinyl chloride. UN, 1 = Reference Standards. UN, 2
+    = Reference Standards. COAINID, 1 = 1.. COAINID, 2 = Reference standards cited
+    in this Section refer to the current reference standard published at the time
+    of the latest revision date logged at the end of this Section unless a date is
+    cited.. , 1 = 2.. , 2 = specifically Texas Department of Transportation, Standard
+    Specifications for Construction and Maintenance of Highways, Streets and Bridges
+    (TxDOT):. , 1 = a.. , 2 = Item 684, Traffic Signal Cables. 3. TxDOT Departmental
+    Materials Specification:. , 1 = 4.. , 2 = Underwriters Laboratories, Inc.. , 1
+    = . , 2 = (UL).. , 1 = 5.. , 2 = International Municipal Signal Association (IMSA)..
+    , 1 = ADMINISTRATIVE REQUIREMENTS [NOT USED]. , 2 = ADMINISTRATIVE REQUIREMENTS
+    [NOT USED]. , 1 = SUBMITTALS. , 2 = SUBMITTALS.'
+- source_sentence: How do the Dimensional Standards apply to our development project
+    in the specified zoning districts?
+  sentences:
+  - 'v = Entire Site = Development Impact Area Only:
+
+    . . Dimensional Standards, DDC Reference = Subchapter 3: Zoning Districts. . .
+    Dimensional Standards, New A Construction = Vv. . . Dimensional Standards, Minor:
+    Tier 1 = 0). . . Dimensional Standards, Major: Tier 2 = v. Land-Disturbing Activities,
+    DDC Reference = 7.2. Land-Disturbing Activities, New A Construction = All development,
+    see Section 7.2. Land-Disturbing Activities, Minor: Tier 1 = All development,
+    see Section 7.2. Land-Disturbing Activities, Major: Tier 2 = All development,
+    see Section 7.2. Environmentally Sensitive Areas, DDC Reference = 74. Environmentally
+    Sensitive Areas, New A Construction = All development, see Section 7.4. Environmentally
+    Sensitive Areas, Minor: Tier 1 = . Environmentally Sensitive Areas, Major: Tier
+    2 = . Drainage, DDC Reference = 74. Drainage, New A Construction = All development,
+    see Section 7.4. Drainage, Minor: Tier 1 = . Drainage, Major: Tier 2 = . Water
+    and Wastewater, DDC Reference = 7.6. Water and Wastewater, New A Construction
+    = All development,'
+  - '3. Step 3: Determination of Procedure:
+
+    Upon receipt of a complete application for a Certificate of Design Consistency,
+    the Director must determine the appropriate review procedure prescribed by sections
+    2.10.1.D.3.a-d below.
+
+    Denton, Texas — Denton Development Code
+
+    Print Date: August 30, 2024
+
+    136'
+  - '4B A. Tests and Inspections:
+
+    CITY OF DENTON STANDARD CONSTRUCTION SPECIFICATION DOCUMENTS Revised October 22,
+    2020
+
+    Effective
+
+    July 1, 2024
+
+    [Insert Bid Number] [Insert Engineering Project Number]
+
+    3.5
+
+    REPAIR
+
+    33 31 23
+
+    COMBINATION AIR VALVE ASSEMBLIES FOR SEWER FORCE MAINS
+
+    Page 6 of 7
+
+    1. Testing and inspection of Combination Air Valves shall be in accordance with
+    AWWA C512.'
+- source_sentence: How do the regulations for Light Industrial (LI) and Heavy Industrial
+    (HI) districts differ in terms of permitted uses and development standards?
+  sentences:
+  - 'This print version includes the following Code amendments::
+
+    Other Nonresidential Districts. , 3 = . , 1 = 3.5.1 GO - General Office... , 2
+    = 3.5.1 GO - General Office... , 3 = . , 1 = - LI - Light Industrial. , 2 = -
+    LI - Light Industrial. , 3 = . , 1 = 3.5.2. , 2 = . , 3 = . , 1 = 3.5.3.. , 2
+    = HI - Heavy Industrial. , 3 = . , 1 = 3.5.4. , 2 = PF - Public Facilities.. ,
+    3 = oa. , 1 = Summary -. , 2 = Summary -. , 3 = . 3.6, 1 = PD Planned Development
+    District. 3.6, 2 = PD Planned Development District. 3.6, 3 = . , 1 = 3.6.1 Purpose...
+    seeesesnsnseeeenens Review Procedure ... esse. , 2 = 3.6.1 Purpose... seeesesnsnseeeenens
+    Review Procedure ... esse. , 3 = . , 1 = 3.6.2. , 2 = .'
+  - '1.1 SUMMARY:
+
+    AADUNAPWNK, 1 = . AADUNAPWNK, 2 = . AADUNAPWNK, 3 = ensure the conduit is clean
+    and free from obstructions.. , 1 = . , 2 = . , 3 = 17) Conduits shall be placed
+    in an open trench at a minimum 24 inches (612. , 1 = . , 2 = . , 3 = mm) depth
+    below the curb grade in the sidewalk areas, or 18 inches (450 mm) below the finished
+    street grade in the street area.. PW CUO, 1 = . PW CUO, 2 = 18). PW CUO, 3 = Conduit
+    placed for concrete encasement shall be secured and supported in. HP, 1 = . HP,
+    2 = . HP, 3 = such a manner the alignment will not be disturbed during placement
+    of the. , 1 = . , 2 = . , 3 = concrete.. CPAIANDNAHBPWNYRK, 1 = . CPAIANDNAHBPWNYRK,
+    2 = . CPAIANDNAHBPWNYRK, 3 = Noconcrete shall be until all conduit ends have been
+    and. , 1 = . ,'
+  - 'E. Water Control:
+
+    1. Surface Water
+
+    a. Furnish all materials and equipment and perform all incidental work required
+    to direct surface water away from the excavation.'
+- source_sentence: How can I ensure that the curing compound we receive at the job
+    site meets the required specifications with the manufacturer's original containers
+    and labels intact?
+  sentences:
+  - 'B. Exemptions:
+
+    Unless otherwise provided in this DDC, the following shall be exempt from the
+    provisions of this Section 7.3: Land-Disturbing Activities:
+
+    1. Grading and clearing in emergency situations involving immediate danger to
+    life and property or substantial fire hazards;
+
+    2. Any activity where the total volume of material disturbed, stored, disposed
+    of or used as fill does not exceed 25 cubic yards and the area disturbed does
+    not exceed 2,000 square feet, provided it does not obstruct a watercourse and
+    is not located in a floodplain or other environmentally sensitive area;
+
+    3. Soil-disturbing activities, excluding tree removal, that are associated with
+    normal agricultural crop operations; or
+
+    4. Stockpiling and handling of earth material associated with commercial quarry
+    and landfill operations licensed under the state.
+
+    Denton, Texas — Denton Development Code
+
+    Print Date: August 30, 2024
+
+    353'
+  - '8. Curing:
+
+    03 00 00
+
+    CONCRETE AND CONCRETE REINFORCING
+
+    Page 10 of 18
+
+    6) Curing compound to be delivered to the job site in the manufacturer''s original
+    containers only, with original label containing the following:
+
+    a) Manufacturer''s name
+
+    b) Trade name of the material
+
+    c) Batch number or symbol with which test samples may be correlated'
+  - '2. For Large Wind Energy Systems:
+
+    a. The minimum acreage for a large wind system shall be established based on the
+    setbacks of the turbine(s) and the height of the turbine(s);
+
+    b. All turbines located within the same large wind system property shall be of
+    a similar tower design, including the type, number of blades, and direction of
+    blade rotation;
+
+    c. Large wind systems shall be setback at least one and one-half times the height
+    of the turbine and rotor diameter from the property line. Large wind systems shall
+    also be setback at least one and one-half times the height of the turbine from
+    above ground telephone, electrical lines, and other uninhabitable structures;
+
+    d. Towers shall not be climbable up to 15 feet above ground level.'
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@1
+- cosine_ndcg@3
+- cosine_ndcg@5
+- cosine_ndcg@10
+- cosine_mrr@1
+- cosine_mrr@3
+- cosine_mrr@5
+- cosine_mrr@10
+- cosine_map@100
+model-index:
+- name: worksphere
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 1024
+      type: dim_1024
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.030697674418604652
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.3986046511627907
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.5774418604651163
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.7881395348837209
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.030697674418604652
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.13286821705426355
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.11548837209302326
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.07881395348837208
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.030697674418604652
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.3986046511627907
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.5774418604651163
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.7881395348837209
+      name: Cosine Recall@10
+    - type: cosine_ndcg@1
+      value: 0.030697674418604652
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@3
+      value: 0.23179382587458858
+      name: Cosine Ndcg@3
+    - type: cosine_ndcg@5
+      value: 0.3040553564598666
+      name: Cosine Ndcg@5
+    - type: cosine_ndcg@10
+      value: 0.37531956376470604
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@1
+      value: 0.030697674418604652
+      name: Cosine Mrr@1
+    - type: cosine_mrr@3
+      value: 0.17515503875969318
+      name: Cosine Mrr@3
+    - type: cosine_mrr@5
+      value: 0.21443410852713862
+      name: Cosine Mrr@5
+    - type: cosine_mrr@10
+      value: 0.24572388335179296
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.2550755013176846
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 768
+      type: dim_768
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.030697674418604652
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.3986046511627907
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.5774418604651163
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.7881395348837209
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.030697674418604652
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.13286821705426355
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.11548837209302326
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.07881395348837208
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.030697674418604652
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.3986046511627907
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.5774418604651163
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.7881395348837209
+      name: Cosine Recall@10
+    - type: cosine_ndcg@1
+      value: 0.030697674418604652
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@3
+      value: 0.23179382587458858
+      name: Cosine Ndcg@3
+    - type: cosine_ndcg@5
+      value: 0.3040553564598666
+      name: Cosine Ndcg@5
+    - type: cosine_ndcg@10
+      value: 0.37531956376470604
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@1
+      value: 0.030697674418604652
+      name: Cosine Mrr@1
+    - type: cosine_mrr@3
+      value: 0.17515503875969318
+      name: Cosine Mrr@3
+    - type: cosine_mrr@5
+      value: 0.21443410852713862
+      name: Cosine Mrr@5
+    - type: cosine_mrr@10
+      value: 0.24572388335179296
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.2550755013176846
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.03
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.39069767441860465
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.5644186046511628
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.781860465116279
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.03
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.13023255813953488
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.11288372093023255
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.0781860465116279
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.03
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.39069767441860465
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.5644186046511628
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.781860465116279
+      name: Cosine Recall@10
+    - type: cosine_ndcg@1
+      value: 0.03
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@3
+      value: 0.22663893445598368
+      name: Cosine Ndcg@3
+    - type: cosine_ndcg@5
+      value: 0.2968091108509391
+      name: Cosine Ndcg@5
+    - type: cosine_ndcg@10
+      value: 0.37060640353852903
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@1
+      value: 0.03
+      name: Cosine Mrr@1
+    - type: cosine_mrr@3
+      value: 0.17096899224806486
+      name: Cosine Mrr@3
+    - type: cosine_mrr@5
+      value: 0.20909689922481253
+      name: Cosine Mrr@5
+    - type: cosine_mrr@10
+      value: 0.2416426725729079
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.2512032580492767
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.029534883720930234
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.3774418604651163
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.5502325581395349
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.7644186046511627
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.029534883720930234
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.1258139534883721
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.11004651162790699
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.07644186046511628
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.029534883720930234
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.3774418604651163
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.5502325581395349
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.7644186046511627
+      name: Cosine Recall@10
+    - type: cosine_ndcg@1
+      value: 0.029534883720930234
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@3
+      value: 0.21910859618189715
+      name: Cosine Ndcg@3
+    - type: cosine_ndcg@5
+      value: 0.2887354612410299
+      name: Cosine Ndcg@5
+    - type: cosine_ndcg@10
+      value: 0.3613007541197287
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@1
+      value: 0.029534883720930234
+      name: Cosine Mrr@1
+    - type: cosine_mrr@3
+      value: 0.16538759689922747
+      name: Cosine Mrr@3
+    - type: cosine_mrr@5
+      value: 0.20312015503876593
+      name: Cosine Mrr@5
+    - type: cosine_mrr@10
+      value: 0.23504817275747772
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.2452995067602724
+      name: Cosine Map@100
+---
+
+# worksphere
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+- **Language:** en
+- **License:** apache-2.0
+
+### 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': True}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## 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("sabber/worksphere-regulations-embedding_bge")
+# Run inference
+sentences = [
+    "How can I ensure that the curing compound we receive at the job site meets the required specifications with the manufacturer's original containers and labels intact?",
+    "8. Curing:\n03 00 00\nCONCRETE AND CONCRETE REINFORCING\nPage 10 of 18\n6) Curing compound to be delivered to the job site in the manufacturer's original containers only, with original label containing the following:\na) Manufacturer's name\nb) Trade name of the material\nc) Batch number or symbol with which test samples may be correlated",
+    '2. For Large Wind Energy Systems:\na. The minimum acreage for a large wind system shall be established based on the setbacks of the turbine(s) and the height of the turbine(s);\nb. All turbines located within the same large wind system property shall be of a similar tower design, including the type, number of blades, and direction of blade rotation;\nc. Large wind systems shall be setback at least one and one-half times the height of the turbine and rotor diameter from the property line. Large wind systems shall also be setback at least one and one-half times the height of the turbine from above ground telephone, electrical lines, and other uninhabitable structures;\nd. Towers shall not be climbable up to 15 feet above ground level.',
+]
+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.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Datasets: `dim_1024`, `dim_768`, `dim_512` and `dim_256`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | dim_1024   | dim_768    | dim_512    | dim_256    |
+|:--------------------|:-----------|:-----------|:-----------|:-----------|
+| cosine_accuracy@1   | 0.0307     | 0.0307     | 0.03       | 0.0295     |
+| cosine_accuracy@3   | 0.3986     | 0.3986     | 0.3907     | 0.3774     |
+| cosine_accuracy@5   | 0.5774     | 0.5774     | 0.5644     | 0.5502     |
+| cosine_accuracy@10  | 0.7881     | 0.7881     | 0.7819     | 0.7644     |
+| cosine_precision@1  | 0.0307     | 0.0307     | 0.03       | 0.0295     |
+| cosine_precision@3  | 0.1329     | 0.1329     | 0.1302     | 0.1258     |
+| cosine_precision@5  | 0.1155     | 0.1155     | 0.1129     | 0.11       |
+| cosine_precision@10 | 0.0788     | 0.0788     | 0.0782     | 0.0764     |
+| cosine_recall@1     | 0.0307     | 0.0307     | 0.03       | 0.0295     |
+| cosine_recall@3     | 0.3986     | 0.3986     | 0.3907     | 0.3774     |
+| cosine_recall@5     | 0.5774     | 0.5774     | 0.5644     | 0.5502     |
+| cosine_recall@10    | 0.7881     | 0.7881     | 0.7819     | 0.7644     |
+| cosine_ndcg@1       | 0.0307     | 0.0307     | 0.03       | 0.0295     |
+| cosine_ndcg@3       | 0.2318     | 0.2318     | 0.2266     | 0.2191     |
+| cosine_ndcg@5       | 0.3041     | 0.3041     | 0.2968     | 0.2887     |
+| **cosine_ndcg@10**  | **0.3753** | **0.3753** | **0.3706** | **0.3613** |
+| cosine_mrr@1        | 0.0307     | 0.0307     | 0.03       | 0.0295     |
+| cosine_mrr@3        | 0.1752     | 0.1752     | 0.171      | 0.1654     |
+| cosine_mrr@5        | 0.2144     | 0.2144     | 0.2091     | 0.2031     |
+| cosine_mrr@10       | 0.2457     | 0.2457     | 0.2416     | 0.235      |
+| cosine_map@100      | 0.2551     | 0.2551     | 0.2512     | 0.2453     |
+
+<!--
+## 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: 17,198 training samples
+* Columns: <code>question</code> and <code>context</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | question                                                                          | context                                                                             |
+  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                              |
+  | details | <ul><li>min: 14 tokens</li><li>mean: 26.6 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 140.8 tokens</li><li>max: 259 tokens</li></ul> |
+* Samples:
+  | question                                                                                                                                                                                                            | context                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Are there any specific guidelines or requirements for the installation of tree supports as outlined in the regulations?</code>                                                                                | <code>SECTION 32 93 00:<br>Cast-in-Place 31 25 14 - Erosion and 32 13 13 - Concrete Paving. 32 13 16 - Decorative Concrete. a. Measurement 1) Measured per each Tree planted. b. Payment 1) The work performed and materials and measured as provided under price bid per each for Tree 2) Various caliper inches. The price bid shall include: 1) Furnishing and installing Tree as 2) Preparing excavation pit 3) Topsoil, fertilizer, mulch, and planting mix, 1 = . , 1 = Tree. , 1 = furnished in accordance with this item "Measurement" will be paid for at the unit for:. planted, 1 = . specified, 1 = . by the Drawings, 1 = . supports, 1 = . [Insert Bid Number], 1 = . [Insert, 1 = . 4), 1 = Plant. Number], 1 = Number]. Engineering Project, 1 = Engineering Project<br>Effective July 1, 2024<br>32 93 00<br>PLANTINGS<br>Page 2 of 24<br>eee<br>BER<br>BPRERR</code>                                                                                                                  |
+  | <code>What specific information do I need to include in my application to meet the standards for grouted installations?</code>                                                                                      | <code>1.1 SUMMARY:<br>= . 36, 2 = . 36, 3 = (1) requirements a qualified testing laboratory.. 37, 1 = . 37, 2 = . 37, 3 = Submit a minimum of 3 other similar projects where the proposed grout mix. 38, 1 = . 38, 2 = . 38, 3 = design was used.. 39 40, 1 = . 39 40, 2 = . 39 40, 3 = anticipated volumes of grout to be pumped for each. , 1 = . , 2 = . , 3 = Submit application and reach grouted.. 41, 1 = 4.. 41, 2 = . 41, 3 = Additional requirements for installations of carrier pipe 24-inch and larger:. 42, 1 = . 42, 2 = a.. 42, 3 = Submit work plan describing the carrier pipe installation equipment, materials. 43 44, 1 = . 43 44, 2 = b.. 43 44, 3 = employed. For installations without holding jacks or a restrained spacer, provide buoyant<br>CITY OF DENTON STANDARD CONSTRUCTION SPECIFICATION DOCUMENTS Revised October 22, 2020 Effective July 1, 2024<br>[Insert Engineering Project Number] [Insert Bid Number]<br>eK<br>BWN<br>nA<br>20<br>21<br>22<br>23<br>24</code> |
+  | <code>In the event of a quasi judicial hearing, who else besides the site owner(s) should we inform about the decision notification process, and how do we manage their requests for a copy of the decision?</code> | <code>Notice of Decision:<br>1. Within 10 days after a final decision on an application, the Director shall provide written notification of the decision, unless the applicant was present at the meeting where the decision was made or required by law.<br>2. If the review involves a quasi-judicial hearing, the Director shall, within 10 days after a final decision on the application, provide a written notification of the decision to the owner(s) of the subject site (unless the applicant was present at the meeting where the decision was made or required by law), and any other person that submitted a written request for a copy of the decision before its effective date.</code>                                                                                                                                                                                                                                                                                                  |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          256
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 16
+- `gradient_accumulation_steps`: 16
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 8
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.1
+- `bf16`: True
+- `tf32`: True
+- `load_best_model_at_end`: True
+- `optim`: adamw_torch_fused
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 16
+- `eval_accumulation_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 8
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `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`: True
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: True
+- `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`: True
+- `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_fused
+- `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
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch      | Step    | Training Loss | dim_1024_cosine_ndcg@10 | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 |
+|:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|
+| 0.2974     | 10      | 2.3168        | -                       | -                      | -                      | -                      |
+| 0.5948     | 20      | 1.2839        | -                       | -                      | -                      | -                      |
+| 0.8922     | 30      | 0.6758        | -                       | -                      | -                      | -                      |
+| 0.9814     | 33      | -             | 0.3592                  | 0.3592                 | 0.3556                 | 0.3496                 |
+| 1.1896     | 40      | 0.4651        | -                       | -                      | -                      | -                      |
+| 1.4870     | 50      | 0.3707        | -                       | -                      | -                      | -                      |
+| 1.7844     | 60      | 0.2941        | -                       | -                      | -                      | -                      |
+| 1.9926     | 67      | -             | 0.3732                  | 0.3732                 | 0.3699                 | 0.3601                 |
+| 2.0818     | 70      | 0.2651        | -                       | -                      | -                      | -                      |
+| 2.3792     | 80      | 0.2341        | -                       | -                      | -                      | -                      |
+| 2.6766     | 90      | 0.2093        | -                       | -                      | -                      | -                      |
+| 2.9740     | 100     | 0.1812        | 0.3747                  | 0.3747                 | 0.3718                 | 0.3626                 |
+| 3.2714     | 110     | 0.1717        | -                       | -                      | -                      | -                      |
+| 3.5688     | 120     | 0.1496        | -                       | -                      | -                      | -                      |
+| 3.8662     | 130     | 0.1472        | -                       | -                      | -                      | -                      |
+| 3.9851     | 134     | -             | 0.3742                  | 0.3742                 | 0.3727                 | 0.3628                 |
+| 4.1636     | 140     | 0.1304        | -                       | -                      | -                      | -                      |
+| 4.4610     | 150     | 0.1229        | -                       | -                      | -                      | -                      |
+| 4.7584     | 160     | 0.1085        | -                       | -                      | -                      | -                      |
+| **4.9963** | **168** | **-**         | **0.3745**              | **0.3745**             | **0.3717**             | **0.361**              |
+| 5.0558     | 170     | 0.1144        | -                       | -                      | -                      | -                      |
+| 5.3532     | 180     | 0.1088        | -                       | -                      | -                      | -                      |
+| 5.6506     | 190     | 0.0937        | -                       | -                      | -                      | -                      |
+| 5.9480     | 200     | 0.1023        | -                       | -                      | -                      | -                      |
+| 5.9777     | 201     | -             | 0.3749                  | 0.3749                 | 0.3704                 | 0.3603                 |
+| 6.2454     | 210     | 0.0942        | -                       | -                      | -                      | -                      |
+| 6.5428     | 220     | 0.0919        | -                       | -                      | -                      | -                      |
+| 6.8401     | 230     | 0.0939        | -                       | -                      | -                      | -                      |
+| 6.9888     | 235     | -             | 0.3755                  | 0.3755                 | 0.3705                 | 0.3603                 |
+| 7.1375     | 240     | 0.0925        | -                       | -                      | -                      | -                      |
+| 7.4349     | 250     | 0.0928        | -                       | -                      | -                      | -                      |
+| 7.7323     | 260     | 0.0869        | -                       | -                      | -                      | -                      |
+| 7.8513     | 264     | -             | 0.3753                  | 0.3753                 | 0.3706                 | 0.3613                 |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.11.10
+- Sentence Transformers: 3.3.1
+- Transformers: 4.41.2
+- PyTorch: 2.4.1+cu124
+- Accelerate: 1.3.0
+- Datasets: 2.19.1
+- 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",
+}
+```
+
+#### MatryoshkaLoss
+```bibtex
+@misc{kusupati2024matryoshka,
+    title={Matryoshka Representation Learning},
+    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
+    year={2024},
+    eprint={2205.13147},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+#### 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

@@ -0,0 +1,32 @@
+{
+  "_name_or_path": "BAAI/bge-base-en-v1.5",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.41.2",
+    "pytorch": "2.4.1+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dbd86397a978d25b841543e5d2509589bfde25b3162e025d0f0a4a4c10bbb510
+size 437951328

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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

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

@@ -0,0 +1,57 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
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+  "do_lower_case": true,
+  "mask_token": "[MASK]",
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+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}