Update README

README.md

@@ -1,44 +1,68 @@
 ---
 library_name: keras
+license: mit
+datasets:
+- karpathy/tiny_shakespeare
+metrics:
+- accuracy
+pipeline_tag: text-generation
+tags:
+- lstm
 ---
 
 ## Model description
 
-More information needed
+LSTM trained on Andrej Karpathy's [`tiny_shakespeare`](https://huggingface.co/datasets/karpathy/tiny_shakespeare) dataset, from his blog post, [The Unreasonable Effectiveness of Recurrent Neural Networks](https://karpathy.github.io/2015/05/21/rnn-effectiveness/).
 
 ## Intended uses & limitations
 
-More information needed
+The model predicts the next character based on a variable-length input sequence. After `18` epochs of training, the model is generating text that is somewhat coherent.
+
+```py
+def generate_text(model, encoder, text, n):
+    vocab = encoder.get_vocabulary()
+    generated_text = text
+    for _ in range(n):
+        encoded = encoder([generated_text])
+        pred = model.predict(encoded, verbose=0)
+        pred = tf.squeeze(tf.argmax(pred, axis=-1)).numpy()
+        generated_text += vocab[pred]
+    return generated_text
+
+sample = "M"
+print(generate_text(model, encoder, sample, 100))
+```
+
+```
+MQLUS:
+I will be so that the street of the state,
+And then the street of the street of the state,
+And
+```
 
 ## Training and evaluation data
 
-More information needed
+[![https://example.com](https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg)](https://wandb.ai/adamelliotfields/shakespeare)
 
 ## Training procedure
 
-### Training hyperparameters
+The dataset consists of various works of William Shakespeare concatenated into a single file. The resulting file consists of individual speeches separated by `\n\n`.
 
-The following hyperparameters were used during training:
-
-| Hyperparameters | Value |
-| :-- | :-- |
-| name | AdamW |
-| weight_decay | 0.001 |
-| clipnorm | None |
-| global_clipnorm | None |
-| clipvalue | None |
-| use_ema | False |
-| ema_momentum | 0.99 |
-| ema_overwrite_frequency | None |
-| jit_compile | False |
-| is_legacy_optimizer | False |
-| learning_rate | 0.0002500000118743628 |
-| beta_1 | 0.9 |
-| beta_2 | 0.999 |
-| epsilon | 1e-07 |
-| amsgrad | False |
-| training_precision | float32 |
+The tokenizer is a Keras `TextVectorization` preprocessor that uses a simple character-based vocabulary.
+
+To construct the training set, `100` characters are taken with the next character used as the target. This is repeated for each character in the text and results in **1,115,294** shuffled training examples.
+
+*TODO: upload encoder*
+
+### Training hyperparameters
 
+| Hyperparameters   | Value     |
+| :---------------- | :-------- |
+| `epochs`          | `18`      |
+| `batch_size`      | `1024`    |
+| `optimizer`       | `AdamW`   |
+| `weight_decay`    | `0.001`   |
+| `learning_rate`   | `0.00025` |
 
  ## Model Plot