Mental Health Harmonisation 1

🚀 SentenceTransformer based on sentence-transformers/all-mpnet-base-v2

This SentenceTransformer model maps sentences and paragraphs to a 768 - dimensional dense vector space. It can be used for various tasks such as semantic textual similarity, semantic search, paraphrase mining, text classification, and clustering.

✨ Features

• High - dimensional Embedding: Maps text to a 768 - dimensional vector space.
• Multiple Applications: Suitable for semantic similarity, search, and more.
• Fine - tuned: Based on the sentence-transformers/all-mpnet-base-v2 model.

📦 Installation

First, you need to install the Sentence Transformers library:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'I am not good at expressing my true feelings by the way I talk and look.',
    'Felt nervous or anxious?',
    'Experienced sleep disturbances?',
]
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]

📚 Documentation

Model Details

Model Description

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

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()
)

Evaluation

Metrics - Semantic Similarity

• Evaluated with EmbeddingSimilarityEvaluator

Training Details

Training Dataset - Unnamed Dataset

• Size: 2,351 training samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 5 tokens mean: 16.73 tokens max: 47 tokens	min: 5 tokens mean: 14.82 tokens max: 28 tokens	min: 0.0 mean: 0.26 max: 1.0

• Samples:

  sentence1	sentence2	score
  Do you believe in telepathy (mind-reading)?	I believe that there are secret signs in the world if you just know how to look for them.	0.15
  Irritable behavior, angry outbursts, or acting aggressively?	Felt “on edge”?	0.62
  I have some eccentric (odd) habits.	I often have difficulty following what someone is saying to me.	0.0

• Loss: CosineSimilarityLoss with these parameters:

  {
      "loss_fct": "torch.nn.modules.loss.L1Loss"
  }
  

Evaluation Dataset - Unnamed Dataset

• Size: 236 evaluation samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 236 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 6 tokens mean: 16.4 tokens max: 47 tokens	min: 5 tokens mean: 14.76 tokens max: 28 tokens	min: 0.0 mean: 0.29 max: 1.0

• Samples:

  sentence1	sentence2	score
  Feeling afraid as if something awful might happen?	I have trouble following conversations with others.	0.19
  Do you believe in telepathy (mind-reading)?	Feeling jumpy or easily startled?	0.1
  Other people see me as slightly eccentric (odd).	I have felt that there were messages for me in the way things were arranged, like furniture in a room.	0.0

• Loss: CosineSimilarityLoss with these parameters:

  {
      "loss_fct": "torch.nn.modules.loss.L1Loss"
  }
  

Training Hyperparameters

Non - Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 16

All Hyperparameters

Training Logs

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.3.1
• Transformers: 4.47.1
• PyTorch: 2.5.1+cu121
• Accelerate: 1.2.1
• Datasets: 3.2.0
• Tokenizers: 0.21.0

📄 Citation

BibTeX - Sentence Transformers

@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",
}