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SURIYA-KP/
nomic-embed-text-v2-moe-fine-tuned-depression-symptoms

Name: nomic-embed-text-v2-moe-fine-tuned-depression-symptoms
Availability: InStock

safetensors

nomic_bert

symptom-classification

custom_symptom_dataset

mit

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Symptom Embedding Model

This model is fine-tuned from nomic-ai/nomic-embed-text-v2-moe using contrastive learning to better identify and classify medical symptom descriptions.

Model Description

The model was fine-tuned using a contrastive learning approach to improve the embeddings for 21 different symptom types. The goal is to make embeddings of similar symptom descriptions closer in vector space.

Intended Use

This model is designed for:

Symptom classification in medical text
Retrieval of symptom-related information
Medical text analysis

Training Procedure

The model was trained using contrastive learning with pairs of symptom descriptions and queries.

Training Hyperparameters

Batch size: 16
Learning rate: 2e-05
Number of epochs: 1

Evaluation Results

Mean Average Precision (MAP): 0.4332
Mean R-Precision: 0.4416
Mean Precision@10: 0.4484
Mean NDCG@1000: 0.5445

Usage

python

from transformers import AutoTokenizer, AutoModel
import torch

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("SURIYA-KP/nomic-embed-text-v2-moe-fine-tuned-depression-symptoms")
model = AutoModel.from_pretrained("SURIYA-KP/nomic-embed-text-v2-moe-fine-tuned-depression-symptoms")

# Prepare text
text = "I feel worthless and useless."

# Tokenize and generate embedding
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
with torch.no_grad():
    outputs = model(**inputs)

# Mean pooling
attention_mask = inputs["attention_mask"]
token_embeddings = outputs.last_hidden_state
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
embedding = torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

# Now 'embedding' is the vectorized representation of your text
# Use this for similarity comparison, classification, etc.

Limitations

This model is specifically trained for symptom classification and may not perform well on unrelated tasks.