Update README.md

README.md

@@ -1,38 +1,394 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:1602
+- loss:CosineSimilarityLoss
+base_model: sentence-transformers/all-mpnet-base-v2
+widget:
+- source_sentence: Has there been any recent discussion on the trend of women choosing
+    to become mothers later in life?
+  sentences:
+  - British Columbia, Ontario, New Brunswick, Nova Scotia, and Prince Edward Island
+    have fully implemented universal hearing screening programs.
+  - If the first readings exceed the maximum allowable difference, measurements are
+    taken for a second and, if necessary, a third time.
+  - In recent years, practices have shifted and these professionals are now able to
+    observe, assess, and consult on the child’s program at the centre rather than
+    in an office visit.
+- source_sentence: Where can I find more information on facilitating extra-provincial
+    ward adoptions in British Columbia?
+  sentences:
+  - 'You can refer to Practice Directive #2021-01 for more information on facilitating
+    extra-provincial ward adoptions in British Columbia.'
+  - No, a Care Plan is not required if the child/youth has no special service needs.
+  - Licensed ECEC programs may fall under the responsibility of one or more ministries
+    and departments, including education, health, family, and/or social services.
+- source_sentence: What should be done if there are minor differences in openness
+    requests?
+  sentences:
+  - If there are minor differences, it is advised to try to reach an acceptable compromise
+    in a meeting.
+  - Yes, the adoption can be completed in B.C. even if the child is from another province.
+    However, the originating provincial or territorial child welfare authority is
+    responsible for finalizing the adoption.
+  - The new standards establish the breastfed child as the normative model for child
+    growth and development.
+- source_sentence: Does the federal government in Canada manage Early Childhood Education
+    and Care (ECEC)?
+  sentences:
+  - You can call a friend or relative to ask for help.
+  - You can start introducing common food allergens to your baby as they begin eating
+    solid foods. It's best to introduce them one at a time.
+  - A search of the Parents' Registry should be requested at the time the child or
+    youth is registered with the Adoption and Permanency Branch.
+- source_sentence: How can I order a birth certificate in British Columbia?
+  sentences:
+  - The Hague Convention is an international treaty that sets standards to ensure
+    that the best interests of children and youth are protected.
+  - Only the consents of the Director of Adoption and the child/youth aged 12 or over
+    are required.
+  - The L value is -0.4488, the M value is 15.2759, and the S value is 0.08380.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+model-index:
+- name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: pregnancy val
+      type: pregnancy_val
+    metrics:
+    - type: pearson_cosine
+      value: 0.9454219117248748
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8647267521805166
+      name: Spearman Cosine
+---
 
-# Pregnancy Knowledge Base Embedding Model
+# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
 
-This is a fine-tuned version of sentence-transformers/all-mpnet-base-v2 specifically optimized for pregnancy, childbirth, and parenting information with a focus on Canadian healthcare context.
+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 Description
+## Model Details
 
-- **Base Model:** sentence-transformers/all-mpnet-base-v2
-- **Fine-tuned on:** Question-answer pairs related to pregnancy, childbirth, and parenting in Canada
-- **Embedding Dimensions:** 768
-- **Max Sequence Length:** 384
-- **Training Data:** Synthetic question-answer pairs generated from Canadian pregnancy and parenting resources
+### 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 9a3225965996d404b775526de6dbfe85d3368642 -->
+- **Maximum Sequence Length:** 384 tokens
+- **Output Dimensionality:** 768 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': 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
 
-# Load the model
-model = SentenceTransformer('AkshaySandbox/pregnancy-mpnet-embeddings')
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'How can I order a birth certificate in British Columbia?',
+    'Only the consents of the Director of Adoption and the child/youth aged 12 or over are required.',
+    'The L value is -0.4488, the M value is 15.2759, and the S value is 0.08380.',
+]
+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
 
-# Generate embeddings
-embeddings = model.encode(['What are the signs of preeclampsia?', 
-                          'How much parental leave can I get in Canada?'])
+#### Semantic Similarity
+
+* Dataset: `pregnancy_val`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.9454     |
+| **spearman_cosine** | **0.8647** |
+
+<!--
+## 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: 1,602 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: 7 tokens</li><li>mean: 16.16 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 28.61 tokens</li><li>max: 75 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.51</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                                                                                      | sentence_1                                                                                                                                                                                                                                  | label            |
+  |:----------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>What kind of hearing screening programs do other provinces and territories in Canada have?</code>         | <code>Unless an adoption placement has already been secured and a brief interim placement with a caregiver is required, the child should be 6 months of age or younger.</code>                                                              | <code>0.0</code> |
+  | <code>Are there resources available for children with learning disabilities in early childhood programs?</code> | <code>Yes, most PTs dedicate resources, programs or staff to support children with learning disabilities and other special needs.</code>                                                                                                    | <code>1.0</code> |
+  | <code>What is parental leave?</code>                                                                            | <code>Parental leave is a type of benefit that allows parents to take time off work after the birth or adoption of a child. The text mentions it but does not provide specific details about the duration or requirements in Canada.</code> | <code>1.0</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`: 16
+- `per_device_eval_batch_size`: 16
+- `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`: 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
+- `num_train_epochs`: 3
+- `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 | pregnancy_val_spearman_cosine |
+|:-----:|:----:|:-----------------------------:|
+| 1.0   | 101  | 0.8647                        |
+
+
+### Framework Versions
+- Python: 3.13.1
+- Sentence Transformers: 3.4.1
+- Transformers: 4.49.0
+- PyTorch: 2.6.0
+- Accelerate: 1.4.0
+- Datasets: 3.3.2
+- 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",
+}
 ```
 
-## Intended Use
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
 
-This model is designed for retrieval-augmented generation (RAG) systems focused on providing pregnancy and parenting information in the Canadian healthcare context.
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
 
-## Training
+<!--
+## Model Card Contact
 
-The model was fine-tuned using contrastive learning on question-answer pairs with the following parameters:
-- Batch size: 16
-- Learning rate: 2e-05
-- Epochs: 3
-- Loss function: CosineSimilarityLoss
-        
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->