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Rework bart docstrings #994

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Merged
merged 1 commit into from
Apr 19, 2023
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Rework bart docstrings #994

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4 changes: 4 additions & 0 deletions keras_nlp/models/bart/bart_backbone.py
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Original file line number Diff line number Diff line change
Expand Up @@ -77,6 +77,10 @@ class BartBackbone(Backbone):
),
}

# Pretrained BART encoder.
model = keras_nlp.models.BartBackbone.from_preset("bart_base_en")
model(input_data)

# Randomly initialized BART encoder-decoder model with a custom config
model = keras_nlp.models.BartBackbone(
vocabulary_size=50265,
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2 changes: 1 addition & 1 deletion keras_nlp/models/bart/bart_preprocessor.py
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Expand Up @@ -67,7 +67,7 @@ class BartPreprocessor(Preprocessor):

Examples:

Directly calling the layer on data
Directly calling the layer on data.
```python
preprocessor = keras_nlp.models.BartPreprocessor.from_preset("bart_base_en")

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56 changes: 24 additions & 32 deletions keras_nlp/models/bart/bart_tokenizer.py
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Expand Up @@ -52,38 +52,30 @@ class BartTokenizer(BytePairTokenizer):

Examples:

Batched inputs.
>>> vocab = {"<s>": 0, "<pad>": 1, "</s>": 2, "<mask>": 3}
>>> vocab = {**vocab, "a": 4, "Ġquick": 5, "Ġfox": 6}
>>> merges = ["Ġ q", "u i", "c k", "ui ck", "Ġq uick"]
>>> merges += ["Ġ f", "o x", "Ġf ox"]
>>> tokenizer = keras_nlp.models.RobertaTokenizer(
... vocabulary=vocab, merges=merges
... )
>>> tokenizer(["a quick fox", "a fox quick"])
<tf.RaggedTensor [[4, 5, 6], [4, 6, 5]]>

Unbatched input.
>>> vocab = {"<s>": 0, "<pad>": 1, "</s>": 2, "<mask>": 3}
>>> vocab = {**vocab, "a": 4, "Ġquick": 5, "Ġfox": 6}
>>> merges = ["Ġ q", "u i", "c k", "ui ck", "Ġq uick"]
>>> merges += ["Ġ f", "o x", "Ġf ox"]
>>> tokenizer = keras_nlp.models.RobertaTokenizer(
... vocabulary=vocab, merges=merges
... )
>>> tokenizer("a quick fox")
<tf.Tensor: shape=(3,), dtype=int32, numpy=array([4, 5, 6], dtype=int32)>

Detokenization.
>>> vocab = {"<s>": 0, "<pad>": 1, "</s>": 2, "<mask>": 3}
>>> vocab = {**vocab, "a": 4, "Ġquick": 5, "Ġfox": 6}
>>> merges = ["Ġ q", "u i", "c k", "ui ck", "Ġq uick"]
>>> merges += ["Ġ f", "o x", "Ġf ox"]
>>> tokenizer = keras_nlp.models.RobertaTokenizer(
... vocabulary=vocab, merges=merges
... )
>>> tokenizer.detokenize(tokenizer("a quick fox")).numpy().decode('utf-8')
'a quick fox'
```python
# Unbatched input.
tokenizer = keras_nlp.models.BartTokenizer.from_preset(
"bart_base_en",
)
tokenizer("The quick brown fox jumped.")

# Batched input.
tokenizer(["The quick brown fox jumped.", "The fox slept."])

# Detokenization.
tokenizer.detokenize(tokenizer("The quick brown fox jumped."))

# Custom vocabulary.
vocab = {"<s>": 0, "<pad>": 1, "</s>": 2, "<mask>": 3}
vocab = {**vocab, "a": 4, "Ġquick": 5, "Ġfox": 6}
merges = ["Ġ q", "u i", "c k", "ui ck", "Ġq uick"]
merges += ["Ġ f", "o x", "Ġf ox"]
tokenizer = keras_nlp.models.BartTokenizer(
vocabulary=vocab,
merges=merges,
)
tokenizer("The quick brown fox jumped.")
```
"""

def __init__(
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