Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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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+---
+base_model: thenlper/gte-base
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:1439
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: Motors and Generators (manufacturing)
+  sentences:
+  - Generator components
+  - Hydraulic pumps
+  - Positive displacement pumps for oil transport
+- source_sentence: Heat Exchangers and Boilers Manufacturing
+  sentences:
+  - Insulation materials for boilers
+  - Water heaters
+  - Lubricants for roller bearings
+- source_sentence: Industrial Molds And Mold Boxes
+  sentences:
+  - Logistics costs for machinery distribution
+  - Mold release agents
+  - Mold design and engineering services
+- source_sentence: Industrial Patterns
+  sentences:
+  - Group I base oils
+  - Pattern making services
+  - Design patterns in software
+- source_sentence: Lubricating And Similar Oils Not From Petroleum Refineries
+  sentences:
+  - Crude oil extraction costs
+  - Synthetic lubricants
+  - Crude oil
+---
+
+# SentenceTransformer based on thenlper/gte-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [thenlper/gte-base](https://huggingface.co/thenlper/gte-base). 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:** [thenlper/gte-base](https://huggingface.co/thenlper/gte-base) <!-- at revision 5e95d41db6721e7cbd5006e99c7508f0083223d6 -->
+- **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})
+  (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("neel2306/RE-cp-costgen")
+# Run inference
+sentences = [
+    'Lubricating And Similar Oils Not From Petroleum Refineries',
+    'Synthetic lubricants',
+    'Crude oil',
+]
+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: 1,439 training samples
+* Columns: <code>anchor</code>, <code>positives</code>, and <code>negatives</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                           | positives                                                                        | negatives                                                                       |
+  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                           | string                                                                          |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 9.72 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.96 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.0 tokens</li><li>max: 11 tokens</li></ul> |
+* Samples:
+  | anchor                                                                        | positives                                            | negatives                                             |
+  |:------------------------------------------------------------------------------|:-----------------------------------------------------|:------------------------------------------------------|
+  | <code>Other Metal Valve and Pipe Fitting Manufacturing</code>                 | <code>Pipe fittings</code>                           | <code>Rubber gaskets</code>                           |
+  | <code>Fluid Power Pump and Motor Manufacturing: Miscellaneous Receipts</code> | <code>Pneumatic motors</code>                        | <code>Gear pumps</code>                               |
+  | <code>Maintenance and Repair for Commercial Machinery</code>                  | <code>Labor costs for maintenance technicians</code> | <code>Office supplies for administrative tasks</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"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 480 evaluation samples
+* Columns: <code>anchor</code>, <code>positives</code>, and <code>negatives</code>
+* Approximate statistics based on the first 480 samples:
+  |         | anchor                                                                           | positives                                                                        | negatives                                                                        |
+  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                           | string                                                                           |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 10.4 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.97 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.09 tokens</li><li>max: 14 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                 | positives                                         | negatives                                  |
+  |:-----------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------|:-------------------------------------------|
+  | <code>Other Metal Ore Mining</code>                                                                                    | <code>Aluminum ore processing</code>              | <code>Metal alloy production</code>        |
+  | <code>Bituminous Coal And Lignite Surface Mining: Processed Bituminous Coal And Lignite From Surface Operations</code> | <code>Processed Bituminous Coal</code>            | <code>Anthracite Coal</code>               |
+  | <code>Roofing Contractors</code>                                                                                       | <code>Labor costs for roofing installation</code> | <code>Foundation construction costs</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`: 4
+- `per_device_eval_batch_size`: 4
+- `num_train_epochs`: 15
+- `warmup_ratio`: 0.1
+- `batch_sampler`: no_duplicates
+
+#### 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`: 4
+- `per_device_eval_batch_size`: 4
+- `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`: 1.0
+- `num_train_epochs`: 15
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `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`: 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
+- `eval_use_gather_object`: False
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch   | Step | Training Loss | loss   |
+|:-------:|:----:|:-------------:|:------:|
+| 0.1389  | 50   | 0.955         | 0.8155 |
+| 0.2778  | 100  | 0.8643        | 0.6782 |
+| 0.4167  | 150  | 0.6977        | 0.5452 |
+| 0.5556  | 200  | 0.5738        | 0.4514 |
+| 0.6944  | 250  | 0.3365        | 0.5229 |
+| 0.8333  | 300  | 0.3888        | 0.4742 |
+| 0.9722  | 350  | 0.4754        | 0.3900 |
+| 1.1111  | 400  | 0.4109        | 0.4337 |
+| 1.25    | 450  | 0.3081        | 0.3950 |
+| 1.3889  | 500  | 0.3282        | 0.3345 |
+| 1.5278  | 550  | 0.2371        | 0.3538 |
+| 1.6667  | 600  | 0.1282        | 0.4055 |
+| 1.8056  | 650  | 0.1091        | 0.5044 |
+| 1.9444  | 700  | 0.2137        | 0.4423 |
+| 2.0833  | 750  | 0.1169        | 0.4840 |
+| 2.2222  | 800  | 0.1076        | 0.4867 |
+| 2.3611  | 850  | 0.1669        | 0.4859 |
+| 2.5     | 900  | 0.074         | 0.4873 |
+| 2.6389  | 950  | 0.0519        | 0.4409 |
+| 2.7778  | 1000 | 0.0257        | 0.4604 |
+| 2.9167  | 1050 | 0.0749        | 0.4678 |
+| 3.0556  | 1100 | 0.0393        | 0.4564 |
+| 3.1944  | 1150 | 0.0454        | 0.4301 |
+| 3.3333  | 1200 | 0.062         | 0.4882 |
+| 3.4722  | 1250 | 0.0645        | 0.4434 |
+| 3.6111  | 1300 | 0.0115        | 0.4296 |
+| 3.75    | 1350 | 0.0172        | 0.4398 |
+| 3.8889  | 1400 | 0.0429        | 0.4396 |
+| 4.0278  | 1450 | 0.0115        | 0.4482 |
+| 4.1667  | 1500 | 0.0141        | 0.4597 |
+| 4.3056  | 1550 | 0.0032        | 0.4776 |
+| 4.4444  | 1600 | 0.0288        | 0.4693 |
+| 4.5833  | 1650 | 0.006         | 0.4990 |
+| 4.7222  | 1700 | 0.0222        | 0.4693 |
+| 4.8611  | 1750 | 0.0016        | 0.4755 |
+| 5.0     | 1800 | 0.0016        | 0.4367 |
+| 5.1389  | 1850 | 0.0084        | 0.3789 |
+| 5.2778  | 1900 | 0.0013        | 0.3689 |
+| 5.4167  | 1950 | 0.0554        | 0.3591 |
+| 5.5556  | 2000 | 0.0022        | 0.3691 |
+| 5.6944  | 2050 | 0.0019        | 0.3776 |
+| 5.8333  | 2100 | 0.0008        | 0.3802 |
+| 5.9722  | 2150 | 0.0006        | 0.3799 |
+| 6.1111  | 2200 | 0.0007        | 0.3688 |
+| 6.25    | 2250 | 0.0003        | 0.3635 |
+| 6.3889  | 2300 | 0.0125        | 0.3526 |
+| 6.5278  | 2350 | 0.0034        | 0.3338 |
+| 6.6667  | 2400 | 0.0003        | 0.3482 |
+| 6.8056  | 2450 | 0.0149        | 0.3730 |
+| 6.9444  | 2500 | 0.0004        | 0.3932 |
+| 7.0833  | 2550 | 0.0003        | 0.3977 |
+| 7.2222  | 2600 | 0.0007        | 0.3915 |
+| 7.3611  | 2650 | 0.0112        | 0.3923 |
+| 7.5     | 2700 | 0.0006        | 0.3938 |
+| 7.6389  | 2750 | 0.0002        | 0.3986 |
+| 7.7778  | 2800 | 0.0005        | 0.3946 |
+| 7.9167  | 2850 | 0.0003        | 0.3944 |
+| 8.0556  | 2900 | 0.0002        | 0.3996 |
+| 8.1944  | 2950 | 0.0001        | 0.4032 |
+| 8.3333  | 3000 | 0.0001        | 0.4018 |
+| 8.4722  | 3050 | 0.0119        | 0.3811 |
+| 8.6111  | 3100 | 0.0001        | 0.3826 |
+| 8.75    | 3150 | 0.0001        | 0.3844 |
+| 8.8889  | 3200 | 0.0002        | 0.3893 |
+| 9.0278  | 3250 | 0.0001        | 0.3942 |
+| 9.1667  | 3300 | 0.0001        | 0.3963 |
+| 9.3056  | 3350 | 0.0001        | 0.3965 |
+| 9.4444  | 3400 | 0.0144        | 0.3766 |
+| 9.5833  | 3450 | 0.0002        | 0.3792 |
+| 9.7222  | 3500 | 0.0001        | 0.3830 |
+| 9.8611  | 3550 | 0.0001        | 0.3870 |
+| 10.0    | 3600 | 0.0002        | 0.3909 |
+| 10.1389 | 3650 | 0.0001        | 0.3939 |
+| 10.2778 | 3700 | 0.0001        | 0.3943 |
+| 10.4167 | 3750 | 0.0103        | 0.3896 |
+| 10.5556 | 3800 | 0.0001        | 0.3906 |
+| 10.6944 | 3850 | 0.0001        | 0.3929 |
+| 10.8333 | 3900 | 0.0001        | 0.3957 |
+| 10.9722 | 3950 | 0.0001        | 0.3969 |
+| 11.1111 | 4000 | 0.0001        | 0.4016 |
+| 11.25   | 4050 | 0.0001        | 0.4012 |
+| 11.3889 | 4100 | 0.0049        | 0.4058 |
+| 11.5278 | 4150 | 0.0002        | 0.4117 |
+| 11.6667 | 4200 | 0.0001        | 0.4121 |
+| 11.8056 | 4250 | 0.0001        | 0.4131 |
+| 11.9444 | 4300 | 0.0001        | 0.4140 |
+| 12.0833 | 4350 | 0.0001        | 0.4145 |
+| 12.2222 | 4400 | 0.0001        | 0.4145 |
+| 12.3611 | 4450 | 0.0085        | 0.4135 |
+| 12.5    | 4500 | 0.0001        | 0.4112 |
+| 12.6389 | 4550 | 0.0001        | 0.4119 |
+| 12.7778 | 4600 | 0.0001        | 0.4127 |
+| 12.9167 | 4650 | 0.0001        | 0.4140 |
+| 13.0556 | 4700 | 0.0001        | 0.4174 |
+| 13.1944 | 4750 | 0.0001        | 0.4182 |
+| 13.3333 | 4800 | 0.0001        | 0.4187 |
+| 13.4722 | 4850 | 0.0051        | 0.4184 |
+| 13.6111 | 4900 | 0.0001        | 0.4183 |
+| 13.75   | 4950 | 0.0001        | 0.4190 |
+| 13.8889 | 5000 | 0.0001        | 0.4195 |
+| 14.0278 | 5050 | 0.0001        | 0.4199 |
+| 14.1667 | 5100 | 0.0002        | 0.4177 |
+| 14.3056 | 5150 | 0.0001        | 0.4177 |
+| 14.4444 | 5200 | 0.0066        | 0.4153 |
+| 14.5833 | 5250 | 0.0001        | 0.4155 |
+| 14.7222 | 5300 | 0.0001        | 0.4155 |
+| 14.8611 | 5350 | 0.0001        | 0.4155 |
+| 15.0    | 5400 | 0.0001        | 0.4156 |
+
+</details>
+
+### Framework Versions
+- Python: 3.12.6
+- Sentence Transformers: 3.1.0
+- Transformers: 4.44.2
+- PyTorch: 2.4.1+cpu
+- Accelerate: 0.34.2
+- Datasets: 3.0.0
+- 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

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "thenlper/gte-base",
+  "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,
+  "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,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.1.0",
+    "transformers": "4.44.2",
+    "pytorch": "2.4.1+cpu"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:264a678170ba79637dc8469c57ae56711d07f3461e2c561d159f1c66c2c8f283
+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": 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

@@ -0,0 +1,62 @@
+{
+  "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": 128,
+  "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]"
+}