Add new SentenceTransformer model

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:439290
+- loss:DualThresholdEnforcedMNRL1
+base_model: flax-sentence-embeddings/all_datasets_v4_MiniLM-L6
+widget:
+- source_sentence: compression therapy benefits
+  sentences:
+  - 'edema: what is, causes, symptoms, and treatment'
+  - How VIN Data Enhances Market Value Assessments
+  - Daily Iron Intake from Leafy Greens and Fortified Cereals
+- source_sentence: liver function improvement tips
+  sentences:
+  - Antioxidants' Role in Liver Enzyme Regulation
+  - Vitamin K2 and Its Role in Artery Calcification
+  - Fatty Acids' Role in Liver Health
+- source_sentence: back pain prevention exercises
+  sentences:
+  - 'Medication Side Effects: Dizziness, Fatigue, and More'
+  - 'Strengthening Moves: Lunges, Squats, and Leg Raises'
+  - 'Natural Anti-Inflammatories: Foods That May Help'
+- source_sentence: weekly ad shopping tips
+  sentences:
+  - Investor Responses to Surplus Capital in Tech Firms
+  - How Glycemic Index Affects Blood Sugar Levels
+  - Evaluating Household Essentials Promotions in Weekly Circulars
+- source_sentence: vitamin B12 for nerve health
+  sentences:
+  - 'Minoxidil: Side Effects and Use Cases'
+  - Emerging Patterns in Roblox Code Distribution Channels
+  - The Role of Magnesium in Muscle and Nerve Function
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on flax-sentence-embeddings/all_datasets_v4_MiniLM-L6
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [flax-sentence-embeddings/all_datasets_v4_MiniLM-L6](https://huggingface.co/flax-sentence-embeddings/all_datasets_v4_MiniLM-L6). 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:** [flax-sentence-embeddings/all_datasets_v4_MiniLM-L6](https://huggingface.co/flax-sentence-embeddings/all_datasets_v4_MiniLM-L6) <!-- at revision a407cc0b7d85eec9a5617eaf51dbe7b353b0c79f -->
+- **Maximum Sequence Length:** 128 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': 128, '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("Auto-opts/flax-TMNRLB_CVR")
+# Run inference
+sentences = [
+    'vitamin B12 for nerve health',
+    'The Role of Magnesium in Muscle and Nerve Function',
+    'Emerging Patterns in Roblox Code Distribution Channels',
+]
+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.*
+-->
+
+<!--
+## 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: 439,290 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: 5 tokens</li><li>mean: 7.43 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.34 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 0.01</li><li>mean: 0.94</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                   | sentence_1                                                           | label            |
+  |:---------------------------------------------|:---------------------------------------------------------------------|:-----------------|
+  | <code>common UTI misconceptions</code>       | <code>How Antibiotics Like Fosfomycin Target Infections</code>       | <code>1.0</code> |
+  | <code>diuretics for swelling</code>          | <code>Venous Insufficiency and Its Impact on Leg Swelling</code>     | <code>1.0</code> |
+  | <code>pelvic floor exercises benefits</code> | <code>Testosterone Levels and Their Impact on Erectile Health</code> | <code>1.0</code> |
+* Loss: <code>__main__.DualThresholdEnforcedMNRL1</code>
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 90
+- `per_device_eval_batch_size`: 90
+- `num_train_epochs`: 5
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 90
+- `per_device_eval_batch_size`: 90
+- `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
+- `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`: no_duplicates
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+| Epoch  | Step  | Training Loss |
+|:------:|:-----:|:-------------:|
+| 0.1024 | 500   | 2.4422        |
+| 0.2049 | 1000  | 1.8481        |
+| 0.3073 | 1500  | 1.5855        |
+| 0.4098 | 2000  | 1.4325        |
+| 0.5122 | 2500  | 1.332         |
+| 0.6146 | 3000  | 1.2434        |
+| 0.7171 | 3500  | 1.1842        |
+| 0.8195 | 4000  | 1.1338        |
+| 0.9219 | 4500  | 1.0779        |
+| 1.0244 | 5000  | 1.0283        |
+| 1.1268 | 5500  | 0.996         |
+| 1.2293 | 6000  | 0.954         |
+| 1.3317 | 6500  | 0.9362        |
+| 1.4341 | 7000  | 0.895         |
+| 1.5366 | 7500  | 0.8776        |
+| 1.6390 | 8000  | 0.8624        |
+| 1.7414 | 8500  | 0.8438        |
+| 1.8439 | 9000  | 0.8158        |
+| 1.9463 | 9500  | 0.7958        |
+| 2.0488 | 10000 | 0.7779        |
+| 2.1512 | 10500 | 0.754         |
+| 2.2536 | 11000 | 0.7332        |
+| 2.3561 | 11500 | 0.722         |
+| 2.4585 | 12000 | 0.711         |
+| 2.5610 | 12500 | 0.6945        |
+| 2.6634 | 13000 | 0.6965        |
+| 2.7658 | 13500 | 0.6834        |
+| 2.8683 | 14000 | 0.6676        |
+| 2.9707 | 14500 | 0.6635        |
+| 3.0731 | 15000 | 0.6484        |
+| 3.1756 | 15500 | 0.6282        |
+| 3.2780 | 16000 | 0.6297        |
+| 3.3805 | 16500 | 0.6241        |
+| 3.4829 | 17000 | 0.6214        |
+| 3.5853 | 17500 | 0.61          |
+| 3.6878 | 18000 | 0.6106        |
+| 3.7902 | 18500 | 0.6006        |
+| 3.8926 | 19000 | 0.6062        |
+| 3.9951 | 19500 | 0.6022        |
+| 4.0975 | 20000 | 0.5808        |
+| 4.2000 | 20500 | 0.5855        |
+| 4.3024 | 21000 | 0.5852        |
+| 4.4048 | 21500 | 0.5757        |
+| 4.5073 | 22000 | 0.5768        |
+| 4.6097 | 22500 | 0.5715        |
+| 4.7121 | 23000 | 0.5764        |
+| 4.8146 | 23500 | 0.5732        |
+| 4.9170 | 24000 | 0.5777        |
+
+
+### Framework Versions
+- Python: 3.12.3
+- Sentence Transformers: 4.1.0
+- Transformers: 4.52.3
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.7.0
+- Datasets: 3.6.0
+- Tokenizers: 0.21.1
+
+## 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",
+}
+```
+
+<!--
+## 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 +1,25 @@
+{
+  "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": "4.52.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "4.1.0",
+    "transformers": "4.52.3",
+    "pytorch": "2.6.0+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 sha256:6f1f93653b547da707641569ba027299463c935cda0488a460da834b3a8a84be
+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": 128,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "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

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+{
+  "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": 128,
+  "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]"
+}