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: sentence-transformers/all-mpnet-base-v2
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+- pearson_manhattan
+- spearman_manhattan
+- pearson_euclidean
+- spearman_euclidean
+- pearson_dot
+- spearman_dot
+- pearson_max
+- spearman_max
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:25300
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: 16GB DDR4-2666-MHz RDIMM/PC4-21300/single rank/x4/1.2v
+  sentences:
+  - Management advisory services
+  - Printed circuits and integrated circuits and microassemblies
+  - Communications Devices and Accessories
+- source_sentence: GRANDSTREAM GRP 2615 IP TELEFON MAKİNASI Gigabit 5 SIP Hesabı POE
+    + BLUETOOTH + WIFI  IP TELEFON MAKİNASI  / ADAPTÖRLÜ
+  sentences:
+  - Components for information technology or broadcasting or telecommunications
+  - Components for information technology or broadcasting or telecommunications
+  - Communications Devices and Accessories
+- source_sentence: Samsung Galaxy S7 Edge 32GB Black
+  sentences:
+  - Components for information technology or broadcasting or telecommunications
+  - Computer Equipment and Accessories
+  - Communications Devices and Accessories
+- source_sentence: HP.HP 146GB 10k 2.5 SAS HP SP HDD-Factory Intergrated
+  sentences:
+  - Components for information technology or broadcasting or telecommunications
+  - Communications Devices and Accessories
+  - Components for information technology or broadcasting or telecommunications
+- source_sentence: COAXIAL CABLE/COAXIAL CABLE
+  sentences:
+  - Data Voice or Multimedia Network Equipment or Platforms and Accessories
+  - Components for information technology or broadcasting or telecommunications
+  - Electronic hardware and component parts and accessories
+model-index:
+- name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: pearson_cosine
+      value: .nan
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: .nan
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: .nan
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: .nan
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: .nan
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: .nan
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: .nan
+      name: Pearson Dot
+    - type: spearman_dot
+      value: .nan
+      name: Spearman Dot
+    - type: pearson_max
+      value: .nan
+      name: Pearson Max
+    - type: spearman_max
+      value: .nan
+      name: Spearman Max
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: test eval
+      type: test-eval
+    metrics:
+    - type: pearson_cosine
+      value: .nan
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: .nan
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: .nan
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: .nan
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: .nan
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: .nan
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: .nan
+      name: Pearson Dot
+    - type: spearman_dot
+      value: .nan
+      name: Spearman Dot
+    - type: pearson_max
+      value: .nan
+      name: Pearson Max
+    - type: spearman_max
+      value: .nan
+      name: Spearman Max
+---
+
+# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision f1b1b820e405bb8644f5e8d9a3b98f9c9e0a3c58 -->
+- **Maximum Sequence Length:** 384 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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
+  (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("alpcansoydas/product-model")
+# Run inference
+sentences = [
+    'COAXIAL CABLE/COAXIAL CABLE',
+    'Electronic hardware and component parts and accessories',
+    'Components for information technology or broadcasting or telecommunications',
+]
+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
+
+#### 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     | nan     |
+| spearman_cosine    | nan     |
+| pearson_manhattan  | nan     |
+| spearman_manhattan | nan     |
+| pearson_euclidean  | nan     |
+| spearman_euclidean | nan     |
+| pearson_dot        | nan     |
+| spearman_dot       | nan     |
+| pearson_max        | nan     |
+| **spearman_max**   | **nan** |
+
+#### Semantic Similarity
+* Dataset: `test-eval`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric             | Value   |
+|:-------------------|:--------|
+| pearson_cosine     | nan     |
+| spearman_cosine    | nan     |
+| pearson_manhattan  | nan     |
+| spearman_manhattan | nan     |
+| pearson_euclidean  | nan     |
+| spearman_euclidean | nan     |
+| pearson_dot        | nan     |
+| spearman_dot       | nan     |
+| pearson_max        | nan     |
+| **spearman_max**   | **nan** |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 25,300 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>texts</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                         | sentence2                                                                        | texts                              |
+  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------------|
+  | type    | string                                                                            | string                                                                           | list                               |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 16.76 tokens</li><li>max: 77 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 7.99 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>size: 2 elements</li></ul> |
+* Samples:
+  | sentence1                                                                    | sentence2                                                                            | texts                                                                                                                                                    |
+  |:-----------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>CISCO.Power Injector for 1100, 1130AG, 1200 1230AG, 1240AG, 521</code> | <code>Power sources</code>                                                           | <code>['CISCO.Power Injector for 1100, 1130AG, 1200 1230AG, 1240AG, 521', 'Power sources']</code>                                                        |
+  | <code>802.11ac W2 Low-Profile Outdoor AP, Internal Ant, E Reg Dom.</code>    | <code>Data Voice or Multimedia Network Equipment or Platforms and Accessories</code> | <code>['802.11ac W2 Low-Profile Outdoor AP, Internal Ant, E Reg Dom.', 'Data Voice or Multimedia Network Equipment or Platforms and Accessories']</code> |
+  | <code>NSO</code>                                                             | <code>Software</code>                                                                | <code>['NSO', 'Software']</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: 5,422 evaluation samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>texts</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                         | sentence2                                                                        | texts                              |
+  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------------|
+  | type    | string                                                                            | string                                                                           | list                               |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 16.96 tokens</li><li>max: 93 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 7.96 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>size: 2 elements</li></ul> |
+* Samples:
+  | sentence1                                                                 | sentence2                                                     | texts                                                                                                                             |
+  |:--------------------------------------------------------------------------|:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------|
+  | <code>MMS SDPA PROXY APPL - GELISTIRME</code>                             | <code>Software</code>                                         | <code>['MMS SDPA PROXY APPL - GELISTIRME', 'Software']</code>                                                                     |
+  | <code>IPHONE 13 PINK 128GB-TUR</code>                                     | <code>Communications Devices and Accessories</code>           | <code>['IPHONE 13 PINK 128GB-TUR', 'Communications Devices and Accessories']</code>                                               |
+  | <code>12FMTPEf( Elite Female ) x 06LCDX Outdor Cable , 5.0mm , 50m</code> | <code>Electrical equipment and components and supplies</code> | <code>['12FMTPEf( Elite Female ) x 06LCDX Outdor Cable , 5.0mm , 50m', 'Electrical equipment and components and supplies']</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 2
+- `warmup_ratio`: 0.1
+- `fp16`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 2
+- `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`: True
+- `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`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | Validation Loss | spearman_max | test-eval_spearman_max |
+|:------:|:----:|:-------------:|:---------------:|:------------:|:----------------------:|
+| 0.0632 | 100  | 4.9949        | 2.0402          | nan          | -                      |
+| 0.1264 | 200  | 1.9907        | 1.8355          | nan          | -                      |
+| 0.1896 | 300  | 1.8898        | 1.9113          | nan          | -                      |
+| 0.2528 | 400  | 1.8334        | 1.7294          | nan          | -                      |
+| 0.3161 | 500  | 1.7497        | 1.7388          | nan          | -                      |
+| 0.3793 | 600  | 1.6786        | 1.6524          | nan          | -                      |
+| 0.4425 | 700  | 1.6914        | 1.6440          | nan          | -                      |
+| 0.5057 | 800  | 1.6303        | 1.6218          | nan          | -                      |
+| 0.5689 | 900  | 1.6388        | 1.6212          | nan          | -                      |
+| 0.6321 | 1000 | 1.6032        | 1.6182          | nan          | -                      |
+| 0.6953 | 1100 | 1.5957        | 1.5945          | nan          | -                      |
+| 0.7585 | 1200 | 1.6303        | 1.5753          | nan          | -                      |
+| 0.8217 | 1300 | 1.5978        | 1.5705          | nan          | -                      |
+| 0.8850 | 1400 | 1.554         | 1.5663          | nan          | -                      |
+| 0.9482 | 1500 | 1.4899        | 1.5525          | nan          | -                      |
+| 1.0114 | 1600 | 1.4792        | 1.5962          | nan          | -                      |
+| 1.0746 | 1700 | 1.4683        | 1.5481          | nan          | -                      |
+| 1.1378 | 1800 | 1.4615        | 1.5256          | nan          | -                      |
+| 1.2010 | 1900 | 1.4395        | 1.5321          | nan          | -                      |
+| 1.2642 | 2000 | 1.3524        | 1.5148          | nan          | -                      |
+| 1.3274 | 2100 | 1.3876        | 1.5356          | nan          | -                      |
+| 1.3906 | 2200 | 1.4376        | 1.4979          | nan          | -                      |
+| 1.4539 | 2300 | 1.4187        | 1.5046          | nan          | -                      |
+| 1.5171 | 2400 | 1.4604        | 1.5011          | nan          | -                      |
+| 1.5803 | 2500 | 1.4194        | 1.4851          | nan          | -                      |
+| 1.6435 | 2600 | 1.4057        | 1.4897          | nan          | -                      |
+| 1.7067 | 2700 | 1.3683        | 1.4921          | nan          | -                      |
+| 1.7699 | 2800 | 1.3333        | 1.4797          | nan          | -                      |
+| 1.8331 | 2900 | 1.3961        | 1.4752          | nan          | -                      |
+| 1.8963 | 3000 | 1.3718        | 1.4693          | nan          | -                      |
+| 1.9595 | 3100 | 1.3263        | 1.4699          | nan          | -                      |
+| 2.0    | 3164 | -             | -               | -            | nan                    |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.2.0
+- Transformers: 4.44.2
+- PyTorch: 2.4.1+cu121
+- Accelerate: 0.34.2
+- Datasets: 3.0.1
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,24 @@
+{
+  "_name_or_path": "sentence-transformers/all-mpnet-base-v2",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.2.0",
+    "transformers": "4.44.2",
+    "pytorch": "2.4.1+cu121"
+  },
+  "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:ce2e42de066ba635ca07374af647d9209b143bb2f0b366c0eafe24827b37cfce
+size 437967672

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": 384,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "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 +1,72 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "104": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "30526": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 384,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}