Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 384,
+  "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

@@ -0,0 +1,493 @@
+---
+base_model: sentence-transformers/all-MiniLM-L6-v2
+datasets:
+- momo22/eng2nep
+language:
+- en
+- ne
+library_name: sentence-transformers
+metrics:
+- negative_mse
+- src2trg_accuracy
+- trg2src_accuracy
+- mean_accuracy
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:1000
+- loss:MSELoss
+- dataset_size:5000
+- dataset_size:8000
+widget:
+- source_sentence: 'The aggressive semi-employed religion workshop of Razzak, (EFP).
+
+    '
+  sentences:
+  - 'मा ग्रिटर भेट्टाउन सकेन वा GDM प्रयोगकर्ताले कार्यान्वयन गर्न सकेन
+
+    '
+  - 'रज्जाकको आक्रामक अर्द्धशतक धर्मशाला, (एएफपी)।
+
+    '
+  - 'त्यसैले मेरो विजयपछि म त्यस्तो अवस्था आउन दिनेछैन।
+
+    '
+- source_sentence: 'The authority is being a constitutional body, it was also empowered
+    by passing the bill from Parliament.
+
+    '
+  sentences:
+  - 'अख्तियार संवैधानिक निकाय त हुँदै हो, त्यसमा पनि संसदबाटै विधेयक पास गरेर अख्तियारलाई
+    अधिकारसम्पन्न पनि गराइएको थियो।
+
+    '
+  - 'म यहूदाका राजा सिदकियाहलाई र उसका मानिसहरूलाई तिनीहरूका शत्रुहरूकहाँ सुम्पिनेछु
+    जसले तिनीहरूलाई मार्न चाहन्छन्। ती सेनाहरू यरूशलेमबाट गइसकेका भएता पनि म तिनीहरूलाई
+    बाबेलका राजाको सेनाहरूकहाँ सुम्पिनेछु।
+
+    '
+  - '– संकटकालको असर न्यायिक क्षेत्रमा मात्रै पर्दैन, समग्र मुलुकमै पर्छ।
+
+    '
+- source_sentence: 'The two-day conference will participate in investors from China,
+    India, Japan, the US, European countries, Britain and other countries, the Federation
+    said.
+
+    '
+  sentences:
+  - 'उनीहरूको जनजीविकाको आधार प्राकृतिक स्रोत रहेको छ।
+
+    '
+  - 'दुई दिनसम्म हुने सम्मेलनमा चीन, भारत, जापान, अमेरिका, युरोपियन देशहरू, बेलायत
+    लगायत देशबाट लगानीकर्ताको सहभागिता गराउने महासंघले जानकारी दिएको छ
+
+    '
+  - 'नयाँ स्न्यापसट लिनका लागि यो बटन क्लिक गर्नुहोस् ।
+
+    '
+- source_sentence: 'Mr Sankey issued a "confession" through his solicitor after Shields
+    had been convicted but then withdrew it.
+
+    '
+  sentences:
+  - 'श्री सान्कीले ढालहरू दोषी भएपछि आफ्नो समाधानकर्तामार्फत "स्वीकृति" जारी गर्नुभयो
+    तर त्यसपछि यसलाई फिर्ता लिनुभयो।
+
+    '
+  - 'कृत्रिम रुपमा पेट्रोलियम पदार्थको मूल्य स्थिर राख्न अनुदान दिदै जाने हो भने नेपाली
+    अर्थतन्त्र एकदिन धराशायी हुनेछ।
+
+    '
+  - 'ओली सरकारले "राष्ट्रियता-राष्ट्रवाद र" आर्थिक सम्ब्रिद्धि "-आर्थिक विकासलाई यसको
+    प्राथमिकताको रूपमा घोषणा गरेको छ।
+
+    '
+- source_sentence: 'We want to use this time to appeal to the American government
+    to see if they can finally close this chapter.
+
+    '
+  sentences:
+  - 'धेरैले घाउ पाए र ओछ्यानमा थिए।
+
+    '
+  - 'नाम यसको अन्तरराष्ट्रिय हलको अद्वितिय डिजाइनबाट स्पष्ट रूपमा प्राप्त हुन्छ, जुन
+    शीर्षकनियम स्पेसबाट बनेको छ, जुन ठूलो गहिराइमा उच्च दबाब बुझ्न सक्षम छ।
+
+    '
+  - 'हामी अमेरिकी सरकारलाई अपील गर्न यसपटक प्रयोग गर्न चाहन्छौं कि उनीहरूले अन्त्यमा
+    यो अध्याय बन्द गर्न सक्छन्।
+
+    '
+model-index:
+- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
+  results:
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: negative_mse
+      value: -0.37439612206071615
+      name: Negative Mse
+  - task:
+      type: translation
+      name: Translation
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: src2trg_accuracy
+      value: 0.0186
+      name: Src2Trg Accuracy
+    - type: trg2src_accuracy
+      value: 0.00835
+      name: Trg2Src Accuracy
+    - type: mean_accuracy
+      value: 0.013474999999999999
+      name: Mean Accuracy
+---
+
+# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the [momo22/eng2nep](https://huggingface.co/datasets/momo22/eng2nep) dataset. It maps sentences & paragraphs to a 384-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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision 8b3219a92973c328a8e22fadcfa821b5dc75636a -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 384 tokens
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [momo22/eng2nep](https://huggingface.co/datasets/momo22/eng2nep)
+- **Languages:** en, ne
+<!-- - **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': 256, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, '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("jangedoo/all-MiniLM-L6-v2-nepali")
+# Run inference
+sentences = [
+    'We want to use this time to appeal to the American government to see if they can finally close this chapter.\n',
+    'हामी अमेरिकी सरकारलाई अपील गर्न यसपटक प्रयोग गर्न चाहन्छौं कि उनीहरूले अन्त्यमा यो अध्याय बन्द गर्न सक्छन्।\n',
+    'नाम यसको अन्तरराष्ट्रिय हलको अद्वितिय डिजाइनबाट स्पष्ट रूपमा प्राप्त हुन्छ, जुन शीर्षकनियम स्पेसबाट बनेको छ, जुन ठूलो गहिराइमा उच्च दबाब बुझ्न सक्षम छ।\n',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# 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
+
+#### Knowledge Distillation
+
+* Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
+
+| Metric           | Value       |
+|:-----------------|:------------|
+| **negative_mse** | **-0.3744** |
+
+#### Translation
+
+* Evaluated with [<code>TranslationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
+
+| Metric            | Value      |
+|:------------------|:-----------|
+| src2trg_accuracy  | 0.0186     |
+| trg2src_accuracy  | 0.0083     |
+| **mean_accuracy** | **0.0135** |
+
+<!--
+## 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
+
+#### momo22/eng2nep
+
+* Dataset: [momo22/eng2nep](https://huggingface.co/datasets/momo22/eng2nep) at [57da8d4](https://huggingface.co/datasets/momo22/eng2nep/tree/57da8d44266896e334c1d8f2528cbbf666fbd0ca)
+* Size: 8,000 training samples
+* Columns: <code>English</code>, <code>Nepali</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | English                                                                            | Nepali                                                                             | label                                |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
+  | type    | string                                                                             | string                                                                             | list                                 |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 26.29 tokens</li><li>max: 130 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 65.39 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>size: 384 elements</li></ul> |
+* Samples:
+  | English                                                                                                                                                     | Nepali                                                                                                                                 | label                                                                                                                              |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------|
+  | <code>But with the origin of feudal practices in the Middle Ages, the practice of untouchability began, as well as discrimination against women.<br></code> | <code>तर मध्ययुगमा सामन्ती प्रथाको उद्भव भएसँगै जसरी छुवाछुत प्रथाको शुरुवात भयो, त्यसैगरी नारी प्रति पनि विभेद गरिन थालियो<br></code> | <code>[-0.05432726442813873, 0.029996933415532112, -0.008532932959496975, -0.035200122743844986, 0.008856767788529396, ...]</code> |
+  | <code>A Pandit was found on the way to Pokhara from Baglung.<br></code>                                                                                     | <code>वाग्लुङ्गबाट पोखरा आउँदा बाटोमा एकजना पण्डित भेटिए।<br></code>                                                                   | <code>[-0.023763148114085197, 0.0959007516503334, -0.11197677254676819, 0.10978179425001144, -0.028137238696217537, ...]</code>    |
+  | <code>He went on: "She ate a perfectly normal and healthy diet.<br></code>                                                                                  | <code>उनी गए: "उनले पूर्ण सामान्य र स्वस्थ आहार खाइन्।<br></code>                                                                      | <code>[0.028130479156970978, 0.030386686325073242, -0.012276170775294304, 0.1316223442554474, -0.01928202621638775, ...]</code>    |
+* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
+
+### Evaluation Dataset
+
+#### momo22/eng2nep
+
+* Dataset: [momo22/eng2nep](https://huggingface.co/datasets/momo22/eng2nep) at [57da8d4](https://huggingface.co/datasets/momo22/eng2nep/tree/57da8d44266896e334c1d8f2528cbbf666fbd0ca)
+* Size: 500 evaluation samples
+* Columns: <code>English</code>, <code>Nepali</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | English                                                                            | Nepali                                                                            | label                                |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------|
+  | type    | string                                                                             | string                                                                            | list                                 |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 26.71 tokens</li><li>max: 213 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 64.1 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>size: 384 elements</li></ul> |
+* Samples:
+  | English                                                                                                                                                                                                        | Nepali                                                                                                                                                                          | label                                                                                                                              |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Chapter 3<br></code>                                                                                                                                                                                     | <code>परिच्छेद–३<br></code>                                                                                                                                                     | <code>[-0.049459926784038544, 0.048675183206796646, 0.016583453863859177, 0.04876156523823738, -0.020754676312208176, ...]</code>  |
+  | <code>The capability of MOF would be strengthened to enable it to efficiently play the lead role in donor coordination, and to secure support from all stakeholders in aid coordination activities.<br></code> | <code>दाताहरूको समन्वयमा नेतृत्वदायीको भूमिका निर्वाह प्रभावकारी ढंगले गर्न अर्थ मन्त्रालयको क्षमता सुदृढ गरिनेछ यसको लागि सबै सरोकारवालाबाट समर्थन प्राप्त गरिनेछ ।<br></code> | <code>[-0.06200315058231354, -0.016507938504219055, -0.029924314469099045, -0.052509162575006485, 0.07746178656816483, ...]</code> |
+  | <code>Polimatrix, Inc. is a system integrator and total solutions provider delivering radiation and nuclear protection and detection.<br></code>                                                               | <code>पोलिमाट्रिक्स, इन्कर्पोरेटिड प्रणाली इन्टिजर र कुल समाधान प्रदायक रेडियो र आणविक संरक्षण र पत्ता लगाउने प्रणाली इन्टिजर र कुल समाधान प्रदायक हो।<br></code>               | <code>[-0.0446796678006649, 0.026428330689668655, -0.09837698936462402, -0.07765442878007889, -0.020364686846733093, ...]</code>   |
+* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `bf16`: True
+- `push_to_hub`: True
+- `hub_model_id`: jangedoo/all-MiniLM-L6-v2-nepali
+- `push_to_hub_model_id`: all-MiniLM-L6-v2-nepali
+
+#### 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`: 64
+- `per_device_eval_batch_size`: 64
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `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`: 1
+- `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`: True
+- `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`: True
+- `resume_from_checkpoint`: None
+- `hub_model_id`: jangedoo/all-MiniLM-L6-v2-nepali
+- `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`: all-MiniLM-L6-v2-nepali
+- `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
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch | Step | Training Loss | loss   | mean_accuracy | negative_mse |
+|:-----:|:----:|:-------------:|:------:|:-------------:|:------------:|
+| 0.4   | 50   | 0.0021        | 0.0019 | 0.0111        | -0.3837      |
+| 0.8   | 100  | 0.002         | 0.0019 | 0.0123        | -0.3794      |
+| 0.4   | 50   | 0.002         | 0.0019 | 0.0130        | -0.3773      |
+| 0.8   | 100  | 0.002         | 0.0019 | 0.0135        | -0.3744      |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.0.1
+- Transformers: 4.42.4
+- PyTorch: 2.3.1+cu121
+- Accelerate: 0.32.1
+- Datasets: 2.21.0
+- 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",
+}
+```
+
+#### MSELoss
+```bibtex
+@inproceedings{reimers-2020-multilingual-sentence-bert,
+    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2020",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/2004.09813",
+}
+```
+
+<!--
+## 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.*
+-->
+
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+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.42.4",
+    "pytorch": "2.3.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

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": 256,
+  "do_lower_case": false
+}