| --- |
| license: apache-2.0 |
| datasets: |
| - 4nkh/theme_data |
| language: |
| - en |
| metrics: |
| - precision |
| - f1 |
| - recall |
| - accuracy |
| base_model: |
| - google-bert/bert-base-uncased |
| pipeline_tag: text-classification |
| library_name: transformers |
| tags: |
| - multi-label |
| - theme_detection |
| - mentorship |
| - entrepreneurship |
| - startup success |
| - json automation |
| --- |
| # Theme classification model (multi-label) |
|
|
| This repository contains a fine-tuned BERT model for classifying short texts into community-oriented themes. The model was trained locally and pushed to the Hugging Face Hub. |
|
|
| Model details |
|
|
| - Model architecture: bert-base-uncased (fine-tuned) |
| - Problem type: multi-label classification |
| - Labels: `mentorship`, `entrepreneurship`, `startup success` |
| - Training data: `train_theme.jsonl` (included) |
| - Final evaluation (example run): |
| - eval_loss: 0.1822 |
| - eval_micro/f1: 1.0 |
| - eval_macro/f1: 1.0 |
| |
| Usage |
| |
| ```python |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| import torch |
| |
| repo = "4nkh/theme_model" |
| tokenizer = AutoTokenizer.from_pretrained(repo) |
| model = AutoModelForSequenceClassification.from_pretrained(repo) |
|
|
| texts = ["Our co-op paired first-time founders with veteran shop owners to troubleshoot setbacks."] |
| inputs = tokenizer(texts, truncation=True, padding=True, return_tensors="pt") |
| with torch.no_grad(): |
| outputs = model(**inputs) |
| logits = outputs.logits |
| probs = torch.sigmoid(logits) |
| preds = (probs >= 0.5).int() |
| print('probs', probs.numpy(), 'preds', preds.numpy()) |
| ``` |
| |
| Notes |
|
|
| - This model uses a threshold of 0.5 for multi-label predictions. Adjust thresholds per-class as needed. |
| - If you want to re-train or fine-tune further, see `train_theme_model.py` in this folder. |
|
|
| License |
|
|
| Specify your license here (e.g., Apache-2.0) or remove this section if you prefer a different license. |
