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1_Pooling/config.json

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+{
+  "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

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:1459
+- loss:CosineSimilarityLoss
+base_model: sentence-transformers/all-MiniLM-L6-v2
+widget:
+- source_sentence: still popular today this fresh fougere fragrance inspired many
+    wannabes
+  sentences:
+  - fruity notes, fig leaf, cedar needle, resins
+  - ginger, mandarin, green tea, hazelnut, amberwood, daffodil, tangerine, metallic
+    effect
+  - mandarin, lavender, green botanics, jasmine, basil, geranium, sage, sandalwood,
+    vetiver, rosewood, amber
+- source_sentence: rose blush cologne 2023 by jo malone london rose blush cologne
+    presents an enchanting bouquet that captures the essence of blooming romance and
+    tropical vitality with an initial sweet hint of luscious litchi and a refreshing
+    touch of herbs this fragrance unfolds into a heart of delicate rose showcasing
+    a radiant femininity the composition is beautifully rounded off with soft musky
+    undertones adding an elegant warmth that lingers on the skin users describe rose
+    blush as vibrant and joyful perfect for both everyday wear and special occasions
+    reviewers appreciate its fresh appeal heralding it as an uplifting scent that
+    evokes feelings of spring and renewal many highlight its moderate longevity making
+    it suitable for those who desire a fragrance that gently permeates without overwhelming
+    whether youre seeking a burst of floral energy or a subtle whisper of sophistication
+    this perfume is sure to leave a delightful impression
+  sentences:
+  - blonde woods, fresh ginger, carnation, green botanics, clover, green tea, white
+    tea, clary sage, mahogany, ambergris, vetiver, fruits, pink grapefruit, frangipani,
+    myrtle, darjeeling tea, mint
+  - yuzu, clary sage, balsam fir, cedar
+  - lychee, basil, rose, musk
+- source_sentence: thank u next by ariana grande is a playful and modern fragrance
+    that captures the essence of youthful exuberance and selfempowerment this charming
+    scent exudes a vibrant sweetness that dances between fruity and creamy notes creating
+    an inviting aura that is both uplifting and comforting users often describe this
+    perfume as deliciously sweet and fun making it perfect for casual wear or a spirited
+    night out the blend is frequently noted for its warm inviting quality evoking
+    a sense of cheerful nostalgia many reviewers highlight its longlasting nature
+    and delightful sillage ensuring that its fragrant embrace stays with you throughout
+    the day perfect for the confident contemporary woman thank u next effortlessly
+    combines the spirited essence of fresh berries with a creamy tropical nuance which
+    is masterfully balanced by an undercurrent of sweet indulgence overall this fragrance
+    is celebrated for its delightful charm and is sure to make a memorable impression
+    wherever you go
+  sentences:
+  - clary sage, citruses
+  - tagetes, gingerbread, white wood, red fruit, spices, creme brulee
+  - cashmeran, lime, myrtle, metallic effect, vetiver, nasturtium, pimento, resins
+- source_sentence: little black dress eau fraiche by avon exudes a lively and refreshing
+    spirit that captivates effortlessly this fragrance opens with a bright burst of
+    citrus that instantly uplifts the mood reminiscent of sunkissed afternoons as
+    it unfolds delicate floral notes weave through creating an elegant bouquet that
+    embodies femininity and charm the scent is anchored by a subtle musk that rounds
+    out the experience providing a warm and inviting backdrop users have praised this
+    fragrance for its fresh and invigorating essence making it perfect for daytime
+    wear many appreciate its lightness and airy quality which is ideal for those seeking
+    a scent that is both playful and sophisticated with a commendable rating of 375
+    out of 5 it has earned accolades for its delightful character and versatility
+    appealing to a broad audience who value a fragrance that feels both chic and approachable
+    overall little black dress eau fraiche is described as an essential contemporary
+    scent for the modern woman effortlessly enhancing any occasion with its vibrant
+    charm
+  sentences:
+  - floral notes, citruses, water lily, lady of the night flower, white musk, clove,
+    creme brulee, mango, fruits, clover, hinoki wood
+  - lemon, may rose, spices, peony, lily of the valley, blackcurrant, raspberry, peach,
+    musk, sandalwood, amber, heliotrope, oud
+  - cyclamen, petitgrain, sesame, thyme, myrrh
+- source_sentence: indulge your senses with comme une evidence limited edition 2008
+    by yves rocher a sophisticated floral fragrance that captures the essence of tranquility
+    and elegance this scent harmoniously blends delicate floral notes with hints of
+    earthy moss creating a fresh and uplifting experience reminiscent of a serene
+    garden in full bloom users describe it as both refreshing and subtle ideal for
+    those seeking a signature scent that exudes femininity without overwhelming presence
+    the composition is said to invoke feelings of serenity and poise making it perfect
+    for daytime wear or special occasions when one desires a touch of grace with an
+    overall rating of 376 the fragrance has garnered appreciation for its longevity
+    and ability to evoke memories of blooming florals intertwined with natural sweetness
+    it strikes a perfect balance appealing to those who cherish a scent that is both
+    light and intricately layered whether strolling through sunlit paths or enjoying
+    quiet moments inside comme une evidence limited edition envelops the wearer in
+    a soothing embrace leaving a lasting impression of refined simplicity
+  sentences:
+  - papyrus, ginger, spices, herbal notes, lemon blossom, green tree accord, ambertonic,
+    lemon leaf oil, cassis, pimento, acacia, citron, gardenia, elemi, black amber,
+    clove, clary sage, ambergris, lime, darjeeling tea, cashmeran, blonde woods
+  - oud, ginger, sea salt, lily, resins
+  - ambertonic, lemon leaf oil, resins, white wood, woody notes, sweet pea, ambergris
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+model-index:
+- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: pearson_cosine
+      value: 0.9339541699697309
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.733406361302126
+      name: Spearman Cosine
+---
+
+# 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). 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 fa97f6e7cb1a59073dff9e6b13e2715cf7475ac9 -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 384 dimensions
+- **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': 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("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'indulge your senses with comme une evidence limited edition 2008 by yves rocher a sophisticated floral fragrance that captures the essence of tranquility and elegance this scent harmoniously blends delicate floral notes with hints of earthy moss creating a fresh and uplifting experience reminiscent of a serene garden in full bloom users describe it as both refreshing and subtle ideal for those seeking a signature scent that exudes femininity without overwhelming presence the composition is said to invoke feelings of serenity and poise making it perfect for daytime wear or special occasions when one desires a touch of grace with an overall rating of 376 the fragrance has garnered appreciation for its longevity and ability to evoke memories of blooming florals intertwined with natural sweetness it strikes a perfect balance appealing to those who cherish a scent that is both light and intricately layered whether strolling through sunlit paths or enjoying quiet moments inside comme une evidence limited edition envelops the wearer in a soothing embrace leaving a lasting impression of refined simplicity',
+    'oud, ginger, sea salt, lily, resins',
+    'papyrus, ginger, spices, herbal notes, lemon blossom, green tree accord, ambertonic, lemon leaf oil, cassis, pimento, acacia, citron, gardenia, elemi, black amber, clove, clary sage, ambergris, lime, darjeeling tea, cashmeran, blonde woods',
+]
+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
+
+#### Semantic Similarity
+
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.934      |
+| **spearman_cosine** | **0.7334** |
+
+<!--
+## 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: 1,459 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                           | sentence_1                                                                        | label                                                          |
+  |:--------|:-------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                               | string                                                                            | float                                                          |
+  | details | <ul><li>min: 12 tokens</li><li>mean: 182.01 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 33.09 tokens</li><li>max: 88 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.25</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | sentence_1                                                                                                            | label            |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>today tomorrow always in love by avon embodying a sense of timeless romance today tomorrow always in love is an enchanting fragrance that strikes a perfect balance between freshness and warmth this captivating scent opens with bright effervescent notes that evoke images of blooming gardens and sunlit moments as the fragrance unfolds it reveals a charming bouquet that celebrates femininity featuring delicate floral elements that wrap around the wearer like a cherished embrace users describe this perfume as uplifting and evocative making it an ideal companion for both everyday wear and special occasions many reviewers appreciate its elegant character highlighting its multifaceted nature that seamlessly transitions from day to night while some find it subtly sweet and playful others cherish its musky undertones which lend a depth that enhances its allure overall with a moderate rating that suggests a solid appreciation among wearers today tomorrow always in love captures the essence of ro...</code> | <code>lotus, neroli, carambola, pomegranate, tuberose, gardenia, tuberose, pepper, musk, woody notes, amber</code>    | <code>1.0</code> |
+  | <code>mankind hero by kenneth cole encapsulates a vibrant and adventurous spirit designed for the modern man who embraces both freshness and sophistication this fragrance unfolds with an invigorating burst reminiscent of a brisk mountain breeze seamlessly paired with a zesty hint of citrus the aromatic heart introduces a soothing edginess where lavender and warm vanilla intertwine creating a balanced yet captivating profile as it settles an inviting warmth emerges enriched by woody undertones that linger pleasantly on the skin users have praised mankind hero for its versatile character suitable for both casual outings and formal occasions many describe it as longlasting and unique appreciating the balanced blend that feels both refreshing and comforting the overall sentiment reflects a sense of confidence and elegance making this scent a cherished addition to a mans fragrance collection it has garnered favorable reviews boasting a solid rating that underscores its appeal embrace the essence ...</code> | <code>mountain air, lemon, coriander, lavender, vanilla, clary sage, plum, musk, coumarin, amberwood, oak moss</code> | <code>1.0</code> |
+  | <code>black essential dark by avon immerse yourself in the captivating allure of black essential dark a fragrance that elegantly marries the depth of aromatic woods with a touch of leathers sensuality this modern scent envelops the wearer in a rich and sophisticated aura exuding confidence and a hint of mystery users describe it as both refreshing and spicy with an invigorating blend that feels perfect for the urban man who embraces lifes more daring adventures crafted with meticulous attention by perfumer mike parrot this fragrance has garnered a solid reputation amongst enthusiasts resulting in a commendable 405 rating from its admirers many find it to be versatile enough for both day and night wear making it an essential companion for various occasions reviewers frequently highlight its longlasting presence creating an inviting and memorable impression with a delicate yet commanding presence black essential dark is ideal for those looking to leave a mark without overpowering the senses wh...</code> | <code>mint, allspice, white tea, amber, herbal notes, pear blossom, armoise, gurgum wood, creme brulee</code>         | <code>0.0</code> |
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `num_train_epochs`: 5
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 5
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+| Epoch  | Step | spearman_cosine |
+|:------:|:----:|:---------------:|
+| 1.0    | 46   | 0.6586          |
+| 1.0870 | 50   | 0.6783          |
+| 2.0    | 92   | 0.7334          |
+| 2.1739 | 100  | 0.7268          |
+| 3.0    | 138  | 0.7400          |
+| 3.2609 | 150  | 0.7400          |
+| 4.0    | 184  | 0.7426          |
+| 4.3478 | 200  | 0.7387          |
+| 5.0    | 230  | 0.7400          |
+| 1.0    | 46   | 0.7387          |
+| 1.0870 | 50   | 0.7387          |
+| 2.0    | 92   | 0.7295          |
+| 2.1739 | 100  | 0.7255          |
+| 3.0    | 138  | 0.7242          |
+| 3.2609 | 150  | 0.7255          |
+| 4.0    | 184  | 0.7124          |
+| 4.3478 | 200  | 0.7216          |
+| 5.0    | 230  | 0.7334          |
+
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 3.3.1
+- Transformers: 4.47.1
+- PyTorch: 2.5.1+cu124
+- Accelerate: 1.2.1
+- Datasets: 3.2.0
+- Tokenizers: 0.21.0
+
+## 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",
+}
+```
+
+<!--
+## 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": "sentence-transformers/all-MiniLM-L6-v2",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.1",
+  "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": "4.47.1",
+    "pytorch": "2.5.1+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:ba73b35d46215a22fea93575ecc59d08a36fadebc77eafaf4c07250bcc852a1d
+size 90864192

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
+}

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 +1,65 @@
+{
+  "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": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 256,
+  "never_split": null,
+  "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]"
+}