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README.md

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+# Text classification demo (Hugging Face)
+
+This repo contains a minimal example to fine-tune a Hugging Face model for text classification.
+
+Quick start (PowerShell):
+
+1. Activate your venv:
+
+```powershell
+& "C:\Users\Humberto Arias\recipe_bot\venv\Scripts\Activate.ps1"
+```
+
+2. Install dependencies:
+
+```powershell
+pip install --upgrade pip
+pip install transformers datasets accelerate evaluate huggingface-hub
+```
+
+3. Smoke test:
+
+```powershell
+python text_classification_demo.py --smoke-test
+```
+
+4. Prepare `data/train.csv` with `text,label` columns and run training:
+
+```powershell
+python text_classification_demo.py --train_file data/train.csv --model_name_or_path bert-base-uncased --output_dir ./outputs
+```
+
+Notes:
+- This example is intentionally minimal for learning. For larger runs, use `accelerate` and GPU instances.
+- To push to the Hub, `huggingface-cli login` then `trainer.push_to_hub()` can be added.
+
+Model on the Hub
+-----------------
+The demo model was pushed to: https://huggingface.co/x2-world/recipe-bert
+
+Example inference (after pushing to Hub):
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+
+model_id = "x2-world/recipe-bert"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForSequenceClassification.from_pretrained(model_id)
+clf = pipeline('text-classification', model=model, tokenizer=tokenizer)
+print(clf('The pizza was great'))
+```