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fb1ee97 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | # 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'))
```
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