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Model: Nokia/nlgp-docstring
Source: Original Platform

README.md

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+---
+language: 
+  - en
+  - code
+tags:
+- code completion
+- code generation
+license: "apache-2.0"
+---
+
+# NLGP docstring model
+
+The NLGP docstring model was introduced in the paper [Natural Language-Guided Programming](https://arxiv.org/abs/2108.05198).  The model was trained on a collection of Jupyter notebooks and can be used to synthesize Python code that addresses a natural language **intent** in a certain code **context** (see the example below). 
+Also see the [NLGP natural](https://huggingface.co/Nokia/nlgp-natural) model.
+
+This work was carried out by a research team in Nokia Bell Labs.
+
+**Context**
+```py
+import matplotlib.pyplot as plt
+
+values = [1, 2, 3, 4]
+labels = ["a", "b", "c", "d"]
+```
+
+**Intent**
+```py
+# plot a bart chart
+```
+
+**Prediction**
+```py
+plt.bar(labels, values)
+plt.show()
+```
+
+## Usage
+
+```py
+import re
+from transformers import GPT2LMHeadModel, GPT2TokenizerFast
+
+# load the model
+tok = GPT2TokenizerFast.from_pretrained("Nokia/nlgp-docstring")
+model = GPT2LMHeadModel.from_pretrained("Nokia/nlgp-docstring") 
+
+# preprocessing functions
+num_spaces = [2, 4, 6, 8, 10, 12, 14, 16, 18]
+def preprocess(context, query):
+    """
+    Encodes context + query as a single string and 
+    replaces whitespace with special tokens <|2space|>, <|4space|>, ...
+    """
+    input_str = f"{context}\n{query} <|endofcomment|>\n"
+    indentation_symbols = {n: f"<|{n}space|>" for n in num_spaces}
+    m = re.match("^[ ]+", input_str)
+    if not m:
+        return input_str
+    leading_whitespace = m.group(0)
+    N = len(leading_whitespace)
+    for n in self.num_spaces:
+        leading_whitespace = leading_whitespace.replace(n * " ", self.indentation_symbols[n])
+    return leading_whitespace + input_str[N:]
+    
+detokenize_pattern = re.compile(fr"<\|(\d+)space\|>")
+def postprocess(output):
+    output = output.split("<|cell|>")[0]
+    def insert_space(m):
+        num_spaces = int(m.group(1))
+        return num_spaces * " "
+    return detokenize_pattern.sub(insert_space, output)
+
+# inference
+code_context = """
+import matplotlib.pyplot as plt
+
+values = [1, 2, 3, 4]
+labels = ["a", "b", "c", "d"]
+"""
+query = "# plot a bar chart"
+
+input_str = preprocess(code_context, query)
+input_ids = tok(input_str, return_tensors="pt").input_ids
+
+max_length = 150 # don't generate output longer than this length
+total_max_length = min(1024 - input_ids.shape[-1], input_ids.shape[-1] + 150) # total = input + output
+
+input_and_output = model.generate(
+    input_ids=input_ids, 
+    max_length=total_max_length,
+    min_length=10,
+    do_sample=False,
+    num_beams=4,
+    early_stopping=True,
+    eos_token_id=tok.encode("<|cell|>")[0]
+)
+
+output = input_and_output[:, input_ids.shape[-1]:] # remove the tokens that correspond to the input_str
+output_str = tok.decode(output[0])
+postprocess(output_str)
+```
+
+## License and copyright
+
+Copyright 2021 Nokia
+
+Licensed under the Apache License 2.0
+
+SPDX-License-Identifier: Apache-2.0