Create README.md

README.md

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+---
+language:
+- en
+license: openrail
+base_model: bigscience/bloom-560m
+tags:
+- text-classification
+- aging
+- social-media
+- reddit
+- generationing
+metrics:
+- f1
+model-index:
+- name: BLOOM-560m-Personal-Sharing-Classification
+  results:
+  - task:
+      type: text-classification
+    metrics:
+    - type: f1
+      value: 0.9599
+---
+
+# Model Card: BLOOM-560m for Personal Sharing Classification
+
+[cite_start]This model is a fine-tuned version of [BLOOM-560m](https://huggingface.co/bigscience/bloom-560m) designed to classify personal experience sharing in social media text[cite: 80, 85]. [cite_start]It was developed to explore how different generations (Baby Boomers and Gen X) express themselves on pseudonymous platforms like Reddit[cite: 56, 144].
+
+## Model Details
+
+- [cite_start]**Model Type:** Large Language Model (Decoder-only) fine-tuned for sequence classification[cite: 80, 85].
+- [cite_start]**Language:** English[cite: 77].
+- [cite_start]**Finetuned from model:** `bigscience/bloom-560m`[cite: 85].
+- [cite_start]**Application:** Sociotechnical research on digital aging and online self-disclosure[cite: 17, 180].
+
+## Intended Use
+
+### Primary Task
+[cite_start]The model classifies individual sentences into one of four categories to analyze domains of self-disclosure in online forums[cite: 80].
+
+### Categories
+* [cite_start]**Health and Wellness (Label 0):** Personal experiences regarding physical/mental health, treatments, or aging-related bodily changes[cite: 80, 81].
+* [cite_start]**Personal Relationships and Identity (Label 1):** Sentences describing social ties, family, friendships, or social identities[cite: 80, 81].
+* [cite_start]**Professional and Financial (Label 2):** Reflections on work, career history, retirement planning, and financial management[cite: 80, 81].
+* [cite_start]**Not Related to Personal Sharing (Label 3):** Non-reflective content, general information, or social pleasantries (excluded from analysis)[cite: 80, 84].
+
+## Training Data
+
+* [cite_start]**Source:** Publicly available posts and comments from the Reddit subreddit `r/AskOldPeople`[cite: 65, 76].
+* [cite_start]**Size:** 2,000 manually labeled sentences (stratified sampling: 500 per category)[cite: 86].
+* [cite_start]**Data Split:** 80% Training, 10% Validation, 10% Test[cite: 86].
+* [cite_start]**Preprocessing:** Sentences were tokenized using the Punkt sentence tokenizer[cite: 77].
+
+## Performance
+
+[cite_start]The model achieved high accuracy on a held-out test set[cite: 87]:
+
+| Metric | Value |
+| :--- | :--- |
+| **F1 Score** | **0.9599** |
+
+## Usage
+
+You can use this model directly with the Hugging Face `transformers` library:
+
+```python
+from transformers import pipeline
+
+classifier = pipeline("text-classification", model="ernchern/personal_info_classification")
+
+text = "I am 67, retired in August, and most basic expenses are covered by Social Security."
+result = classifier(text)
+print(result)