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---
language: en
library_name: tensorflow
tags:
- character-level
- text-generation
- gru
- tensorflow
- keras
pipeline_tag: text-generation
---
# Character-Based Language Model (GRU)
A character-level text generation model trained on ~2,700 business news articles.
Generates text one character at a time using a GRU recurrent neural network.
## Model Architecture
| Component | Details |
|-----------|--------|
| Embedding | 106 chars -> 128d vectors |
| GRU | 512 units, dropout=0.2 |
| Dense | 106 output classes |
| Total params | 1,054,058 |
## Usage
```python
import tensorflow as tf
import json
from huggingface_hub import hf_hub_download
# Download files
model_path = hf_hub_download(repo_id="ismail99/char-based-language-model", filename="model.keras")
vocab_path = hf_hub_download(repo_id="ismail99/char-based-language-model", filename="vocab.json")
# Load vocabulary and build lookup layers
with open(vocab_path) as f:
vocab = json.load(f)
get_ids = tf.keras.layers.StringLookup(vocabulary=vocab, mask_token=None)
get_chars = tf.keras.layers.StringLookup(
vocabulary=get_ids.get_vocabulary(), invert=True, mask_token=None
)
# Load model (requires CharModel class definition)
model = tf.keras.models.load_model(model_path)
```
## Training Details
- **Dataset**: 2,692 business news articles (~4,483,812 characters after cleaning)
- **Sequence length**: 100
- **Epochs**: 30 (with early stopping, patience=5)
- **Optimizer**: Adam
- **Validation split**: 10%
## Limitations
This is a small character-level model trained on a narrow domain (business news).
It produces plausible-looking news-style text but not factually accurate content.
Out-of-domain seeds will produce lower quality output.
license: mit
language:
- en
-
pipeline_tag: text-generation
--- |