README.md

metadata

datasets:
  - suchintikasarkar/sentiment-analysis-for-mental-health
language:
  - en
library_name: transformers
license: apache-2.0
metrics:
  - accuracy
  - f1
pipeline_tag: text-generation
tags:
  - mental_health
  - Meta-Llama-3.1-8B-Instruct

Llama-3.1-8B-Instruct-Mental-Health-Classification

This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct on an suchintikasarkar/sentiment-analysis-for-mental-health dataset.

Tutorial

Get started with the new Llama models and customize Llama-3.1-8B-It to predict various mental health disorders from the text by following the Fine-Tuning Llama 3.1 for Text Classification tutorial.

Use with Transformers

from transformers import AutoTokenizer,AutoModelForCausalLM,pipeline
import torch

model_id = "kingabzpro/Llama-3.1-8B-Instruct-Mental-Health-Classification"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
        model_id,
        return_dict=True,
        low_cpu_mem_usage=True,
        torch_dtype=torch.float16,
        device_map="auto",
        trust_remote_code=True,
)

text = "I'm trapped in a storm of emotions that I can't control, and it feels like no one understands the chaos inside me"
prompt = f"""Classify the text into Normal, Depression, Anxiety, Bipolar, and return the answer as the corresponding mental health disorder label.
text: {text}
label: """.strip()

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipe(prompt, max_new_tokens=2, do_sample=True, temperature=0.1)

print(outputs[0]["generated_text"].split("label: ")[-1].strip())

# Depression

Results

100%|██████████| 300/300 [03:24<00:00,  1.47it/s]

Accuracy: 0.913
Accuracy for label Normal: 0.972
Accuracy for label Depression: 0.913
Accuracy for label Anxiety: 0.667
Accuracy for label Bipolar: 0.800

Classification Report:

              precision    recall  f1-score   support

      Normal       0.92      0.97      0.95       143
  Depression       0.93      0.91      0.92       115
     Anxiety       0.75      0.67      0.71        27
     Bipolar       1.00      0.80      0.89        15

    accuracy                           0.91       300
   macro avg       0.90      0.84      0.87       300
weighted avg       0.91      0.91      0.91       300

Confusion Matrix:

[[139   3   1   0]
 [  5 105   5   0]
 [  6   3  18   0]
 [  1   2   0  12]]