Update README.md with new model card content

README.md

@@ -1,16 +1,203 @@
 ---
 library_name: keras-hub
 ---
-This is a [`Bart` model](https://keras.io/api/keras_hub/models/bart) uploaded using the KerasHub library and can be used with JAX, TensorFlow, and PyTorch backends.
-Model config:
-* **name:** bart_backbone
-* **trainable:** True
-* **vocabulary_size:** 50264
-* **num_layers:** 12
-* **num_heads:** 16
-* **hidden_dim:** 1024
-* **intermediate_dim:** 4096
-* **dropout:** 0.1
-* **max_sequence_length:** 1024
-
-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.
+### Model Overview
+BART encoder-decoder network.
+
+This class implements a Transformer-based encoder-decoder model as
+described in
+["BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension"](https://arxiv.org/abs/1910.13461).
+
+The default constructor gives a fully customizable, randomly initialized BART
+model with any number of layers, heads, and embedding dimensions. To load
+preset architectures and weights, use the `from_preset` constructor.
+
+Disclaimer: Pre-trained models are provided on an "as is" basis, without
+warranties or conditions of any kind. The underlying model is provided by a
+third party and subject to a separate license, available
+[here](https://github.com/facebookresearch/fairseq/).
+
+
+__Arguments__
+
+
+- __vocabulary_size__: int. The size of the token vocabulary.
+- __num_layers__: int. The number of transformer encoder layers and
+    transformer decoder layers.
+- __num_heads__: int. The number of attention heads for each transformer.
+    The hidden size must be divisible by the number of attention heads.
+- __hidden_dim__: int. The size of the transformer encoding and pooler layers.
+- __intermediate_dim__: int. The output dimension of the first Dense layer in
+    a two-layer feedforward network for each transformer.
+- __dropout__: float. Dropout probability for the Transformer encoder.
+- __max_sequence_length__: int. The maximum sequence length that this encoder
+    can consume. If None, `max_sequence_length` uses the value from
+    sequence length. This determines the variable shape for positional
+    embeddings.
+
+### Example Usage
+```python
+import keras
+import keras_hub
+import numpy as np
+```
+
+Use `generate()` to do text generation, given an input context.
+```python
+bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("bart_large_en_cnn")
+bart_lm.generate("The quick brown fox", max_length=30)
+
+# Generate with batched inputs.
+bart_lm.generate(["The quick brown fox", "The whale"], max_length=30)
+```
+
+Compile the `generate()` function with a custom sampler.
+```python
+bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("bart_large_en_cnn")
+bart_lm.compile(sampler="greedy")
+bart_lm.generate("The quick brown fox", max_length=30)
+```
+
+Use `generate()` with encoder inputs and an incomplete decoder input (prompt).
+```python
+bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("bart_large_en_cnn")
+bart_lm.generate(
+    {
+        "encoder_text": "The quick brown fox",
+        "decoder_text": "The fast"
+    }
+)
+```
+
+Use `generate()` without preprocessing.
+```python
+# Preprocessed inputs, with encoder inputs corresponding to
+# "The quick brown fox", and the decoder inputs to "The fast". Use
+# `"padding_mask"` to indicate values that should not be overridden.
+prompt = {
+    "encoder_token_ids": np.array([[0, 133, 2119, 6219, 23602, 2, 1, 1]]),
+    "encoder_padding_mask": np.array(
+        [[True, True, True, True, True, True, False, False]]
+    ),
+    "decoder_token_ids": np.array([[2, 0, 133, 1769, 2, 1, 1]]),
+    "decoder_padding_mask": np.array([[True, True, True, True, False, False]])
+}
+
+bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset(
+    "bart_large_en_cnn",
+    preprocessor=None,
+)
+bart_lm.generate(prompt)
+```
+
+Call `fit()` on a single batch.
+```python
+features = {
+    "encoder_text": ["The quick brown fox jumped.", "I forgot my homework."],
+    "decoder_text": ["The fast hazel fox leapt.", "I forgot my assignment."]
+}
+bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("bart_large_en_cnn")
+bart_lm.fit(x=features, batch_size=2)
+```
+
+Call `fit()` without preprocessing.
+```python
+x = {
+    "encoder_token_ids": np.array([[0, 133, 2119, 2, 1]] * 2),
+    "encoder_padding_mask": np.array([[1, 1, 1, 1, 0]] * 2),
+    "decoder_token_ids": np.array([[2, 0, 133, 1769, 2]] * 2),
+    "decoder_padding_mask": np.array([[1, 1, 1, 1, 1]] * 2),
+}
+y = np.array([[0, 133, 1769, 2, 1]] * 2)
+sw = np.array([[1, 1, 1, 1, 0]] * 2)
+
+bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset(
+    "bart_large_en_cnn",
+    preprocessor=None,
+)
+bart_lm.fit(x=x, y=y, sample_weight=sw, batch_size=2)
+```
+
+## Example Usage with Hugging Face URI
+
+```python
+import keras
+import keras_hub
+import numpy as np
+```
+
+Use `generate()` to do text generation, given an input context.
+```python
+bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("hf://keras/bart_large_en_cnn")
+bart_lm.generate("The quick brown fox", max_length=30)
+
+# Generate with batched inputs.
+bart_lm.generate(["The quick brown fox", "The whale"], max_length=30)
+```
+
+Compile the `generate()` function with a custom sampler.
+```python
+bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("hf://keras/bart_large_en_cnn")
+bart_lm.compile(sampler="greedy")
+bart_lm.generate("The quick brown fox", max_length=30)
+```
+
+Use `generate()` with encoder inputs and an incomplete decoder input (prompt).
+```python
+bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("hf://keras/bart_large_en_cnn")
+bart_lm.generate(
+    {
+        "encoder_text": "The quick brown fox",
+        "decoder_text": "The fast"
+    }
+)
+```
+
+Use `generate()` without preprocessing.
+```python
+# Preprocessed inputs, with encoder inputs corresponding to
+# "The quick brown fox", and the decoder inputs to "The fast". Use
+# `"padding_mask"` to indicate values that should not be overridden.
+prompt = {
+    "encoder_token_ids": np.array([[0, 133, 2119, 6219, 23602, 2, 1, 1]]),
+    "encoder_padding_mask": np.array(
+        [[True, True, True, True, True, True, False, False]]
+    ),
+    "decoder_token_ids": np.array([[2, 0, 133, 1769, 2, 1, 1]]),
+    "decoder_padding_mask": np.array([[True, True, True, True, False, False]])
+}
+
+bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset(
+    "hf://keras/bart_large_en_cnn",
+    preprocessor=None,
+)
+bart_lm.generate(prompt)
+```
+
+Call `fit()` on a single batch.
+```python
+features = {
+    "encoder_text": ["The quick brown fox jumped.", "I forgot my homework."],
+    "decoder_text": ["The fast hazel fox leapt.", "I forgot my assignment."]
+}
+bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("hf://keras/bart_large_en_cnn")
+bart_lm.fit(x=features, batch_size=2)
+```
+
+Call `fit()` without preprocessing.
+```python
+x = {
+    "encoder_token_ids": np.array([[0, 133, 2119, 2, 1]] * 2),
+    "encoder_padding_mask": np.array([[1, 1, 1, 1, 0]] * 2),
+    "decoder_token_ids": np.array([[2, 0, 133, 1769, 2]] * 2),
+    "decoder_padding_mask": np.array([[1, 1, 1, 1, 1]] * 2),
+}
+y = np.array([[0, 133, 1769, 2, 1]] * 2)
+sw = np.array([[1, 1, 1, 1, 0]] * 2)
+
+bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset(
+    "hf://keras/bart_large_en_cnn",
+    preprocessor=None,
+)
+bart_lm.fit(x=x, y=y, sample_weight=sw, batch_size=2)
+```