KingHEA/Bard_1.0

Bard — trained from scratch, pushed to Hugging Face

A complete, minimal project: a custom tokenizer, a small Transformer built by hand, a training loop, and a script to publish the result to the Hugging Face Hub.

Files

File	What it does
tokenizer.py	A word-level tokenizer that learns its own vocabulary from your text — no external files.
model.py	A small Transformer encoder classifier, wrapped as a proper Hugging Face PreTrainedModel.
sample_data.csv	50 labeled example sentences (positive/negative) to get you started.
train.py	Trains the model from scratch on sample_data.csv.
push_to_hub.py	Uploads your trained model to huggingface.co.
MODEL_CARD.md	Becomes the model's README on the Hub.

Steps

1. Install dependencies (locally, or in a free Colab notebook — training is small and fast even on CPU):

   pip install -r requirements.txt
   

2. (Optional) Replace sample_data.csv with your own data. Keep the same two columns: text,label (label is 0 or 1). More rows = a better model — even a few hundred makes a real difference.

3. Train:

   python train.py
   

   This prints loss/accuracy per epoch and saves the trained model to tiny-sentiment-model/.

4. Create a Hugging Face account and access token, if you don't have one: https://huggingface.co/join, then https://huggingface.co/settings/tokens (create a token with "write" access).

5. Log in from the terminal:

   huggingface-cli login
   

6. Edit REPO_ID in push_to_hub.py to "your-username/tiny-sentiment-model".

7. Push it:

   python push_to_hub.py
   

   Your model will be live at https://huggingface.co/your-username/tiny-sentiment-model.

Notes

• This model is trained fully from scratch (random weight initialization) — it does not fine-tune any existing pretrained model. That keeps it small and fast to train, but it will need more data than the 50-row sample to get genuinely good at sentiment analysis.
• Because the model is a custom architecture, anyone loading it later needs model.py alongside the checkpoint (or you can add trust_remote_code support — ask if you'd like help setting that up so AutoModel.from_pretrained(...) works without the file).
• Want to go bigger later? The same pattern (config + PreTrainedModel) scales up — just increase hidden_size, num_layers, and dataset size.