Save model using Keras.

README.md

@@ -1,136 +1,17 @@
+
 ---
-license: mit
-language:
-- en
-metrics:
-- accuracy
-pipeline_tag: text-generation
-tags:
-- text-generation
-- name-generation
 library_name: keras
 ---
 
-# Model Card for Infinitode/TWNGM-OPEN-ARC
-
-Repository: https://github.com/Infinitode/OPEN-ARC/
-
-## Model Description
-
-OPEN-ARC-TWNG is a simple recurrent neural network (RNN) language model developed as part of Infinitode’s OPEN-ARC initiative. It predicts the next token in a sequence using a lightweight architecture suitable for smaller datasets.
-
-**Architecture**:
-
-- **Embedding**: `Embedding(vocab_size, 500)` maps tokens to 500-dimensional vectors.
-- **Recurrent Layer**: `SimpleRNN(50)` processes sequences with 50 recurrent units.
-- **Output Layer**: `Dense(vocab_size, activation='softmax')` produces a probability distribution over the vocabulary.
-- **Framework**: TensorFlow 2.x / Keras
-- **Training Setup**: Compiled with `loss='categorical_crossentropy'`, `optimizer='adam'`, and tracked `accuracy` metric.
-
-## Uses
-
-- Generating creative and fantasy-style weapon or item names.
-- Supporting game design workflows and ideation.
-- Creating randomized content for prototyping or mods.
-
-## Limitations
-
-- May produce implausible or inappropriate names if poorly seeded or prompted.
-- Vocabulary is tailored to Terraria-style names; may not generalize to other genres.
-- Does not enforce real-world constraints like lore consistency, cultural appropriateness, or game balance.
-
-## Training Data
-
-- Dataset: All Terraria Weapons DPS v1.449 dataset from Kaggle.
-- Source URL: https://www.kaggle.com/datasets/acr1209/all-terraria-weapons-dps-v-1449
-- Content: Weapon names and their damage-per-second values used as creative seed text.
-- Size: 395 entries of unique weapon names.
-- Preprocessing: Names tokenized into subword units using SentencePiece BPE.
-
-## Training Procedure
-
-- Tokenizer: SentencePiece BPE trained on the weapon name corpus (vocab size: 500).
-- Batch Size: 128
-- Sequence Length: max_seq_len (based on longest tokenized name, `11`)
-- Optimizer: Adam
-- Loss: categorical_crossentropy
-- Metrics: accuracy
-- Epochs: 500
-- Train/Validation Split: 100% train, 0% validation.
-
-## Evaluation Results
-
-| Metric | Value |
-| ------ | ----- |
-| Train Accuracy | 78.6% |
-| Validation Accuracy | not used |
-| Loss (final) | 0.44 |
-
-## How to Use
-
-```python
-import random
-
-def generate_random_name(min_length=3, max_length=10, temperature=1.0, seed_text=""):
-    # Use a random seed
-    random.seed()
-    
-    if seed_text:
-        # If seed text is provided
-        generated_name = seed_text
-    else:
-        # Randomly select a token from our vocab as our starting token if no seed text is present
-        random_index = random.randint(1, vocab_size-1)
-        random_token = sp.id_to_piece(random_index)
-        generated_name = random_token
-
-    # Generate subsequent subword tokens
-    for _ in range(max_length - 1):
-        # Encode our starting text
-        token_list = sp.encode_as_ids(generated_name)
-        token_list = pad_sequences([token_list], maxlen=max_seq_len-1, padding='pre')
-        
-        # Run prediction
-        predicted = model.predict(token_list, verbose=0)[0]
-
-        # Apply temperature to predictions, helps to varied results
-        predicted = np.log(predicted + 1e-8) / temperature
-        predicted = np.exp(predicted) / np.sum(np.exp(predicted))
-
-        # Sample from the distribution
-        next_index = np.random.choice(range(vocab_size), p=predicted)
-        next_index = int(next_index)
-        next_token = sp.id_to_piece(next_index)
-
-        # Add the predicted token to our output
-        generated_name += next_token
-        
-        # Decode the generated subword tokens into a string
-        decoded_name = sp.decode_pieces(generated_name.split())
-
-        # Stop if end token is predicted (optional, based on your dataset), or stop if max_length is reached
-        if next_token == '' or len(decoded_name) > max_length:
-            break
-
-    # Replace underscores with spaces
-    decoded_name = decoded_name.replace("▁", " ")
-    
-    # Remove stop tokens from the output
-    decoded_name = decoded_name.replace("</s>", "")
-    
-    # Capatilize the first letter of each word
-    generated_name = decoded_name.rsplit(' ', 1)[0]
-    generated_name = generated_name[0].upper() + generated_name[1:]
+This model has been uploaded using the Keras library and can be used with JAX,
+TensorFlow, and PyTorch backends.
 
-    # Split the name and check the last part, make sure that it is not cut off
-    parts = generated_name.split()
-    if parts and len(parts[-1]) < min_length:
-        generated_name = " ".join(parts[:-1])
-    
-    # Strip the output to ensure no extra whitespace
-    return generated_name.strip()
-```
+This model card has been generated automatically and should be completed by the
+model author.
+See [Model Cards documentation](https://huggingface.co/docs/hub/model-cards) for
+more information.
 
-## Contact
+For more details about the model architecture, check out
+[config.json](./config.json).
 
-For questions or issues, open a GitHub issue or reach out at https://infinitode.netlify.app/forms/contact.
+![](./assets/summary_plot.png)

config.json

@@ -0,0 +1 @@
+{"module": "keras", "class_name": "Sequential", "config": {"name": "sequential", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null}, "layers": [{"module": "keras.layers", "class_name": "InputLayer", "config": {"batch_shape": [null, 11], "dtype": "float32", "sparse": false, "name": "input_layer"}, "registered_name": null}, {"module": "keras.layers", "class_name": "Embedding", "config": {"name": "embedding", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null}, "input_dim": 500, "output_dim": 500, "embeddings_initializer": {"module": "keras.initializers", "class_name": "RandomUniform", "config": {"seed": null, "minval": -0.05, "maxval": 0.05}, "registered_name": null}, "embeddings_regularizer": null, "activity_regularizer": null, "embeddings_constraint": null, "mask_zero": false}, "registered_name": null, "build_config": {"input_shape": [null, 11]}}, {"module": "keras.layers", "class_name": "SimpleRNN", "config": {"name": "simple_rnn", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null}, "return_sequences": false, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "zero_output_for_mask": false, "units": 50, "activation": "tanh", "use_bias": true, "kernel_initializer": {"module": "keras.initializers", "class_name": "GlorotUniform", "config": {"seed": null}, "registered_name": null, "shared_object_id": 138835741077008}, "recurrent_initializer": {"module": "keras.initializers", "class_name": "Orthogonal", "config": {"seed": null, "gain": 1.0}, "registered_name": null, "shared_object_id": 138835708245392}, "bias_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null, "shared_object_id": 138835719676560}, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0}, "registered_name": null, "build_config": {"input_shape": [null, 11, 500]}}, {"module": "keras.layers", "class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null}, "units": 500, "activation": "softmax", "use_bias": true, "kernel_initializer": {"module": "keras.initializers", "class_name": "GlorotUniform", "config": {"seed": null}, "registered_name": null}, "bias_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "kernel_regularizer": null, "bias_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "registered_name": null, "build_config": {"input_shape": [null, 50]}}], "build_input_shape": [null, 11]}, "registered_name": null, "build_config": {"input_shape": [null, 11]}, "compile_config": {"optimizer": {"module": "keras.optimizers", "class_name": "Adam", "config": {"name": "adam", "learning_rate": 0.0010000000474974513, "weight_decay": null, "clipnorm": null, "global_clipnorm": null, "clipvalue": null, "use_ema": false, "ema_momentum": 0.99, "ema_overwrite_frequency": null, "loss_scale_factor": null, "gradient_accumulation_steps": null, "beta_1": 0.9, "beta_2": 0.999, "epsilon": 1e-07, "amsgrad": false}, "registered_name": null}, "loss": {"module": "builtins", "class_name": "function", "config": "categorical_crossentropy", "registered_name": "function"}, "loss_weights": null, "metrics": ["accuracy"], "weighted_metrics": null, "run_eagerly": false, "steps_per_execution": 1, "jit_compile": false}}

metadata.json

@@ -0,0 +1 @@
+{"keras_version": "3.8.0", "date_saved": "2025-05-28@06:11:51"}