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