README and diffs for v3.0

README.md

@@ -1312,50 +1312,28 @@ These are the prompt variations:
 Note that MBPP does not have any doctests, so the "removed" and "transform"
 variations are not available for MBPP.
 
-## Example Script
+## Changelog
 
-The following script uses the Salesforce/codegen model to generate Lua
-and MultiPL-E to produce a script with unit tests for luaunit.
-*This example is quite outdated.*
+### Version 3.0
 
-```python
-import datasets
-from transformers import AutoTokenizer, AutoModelForCausalLM
+This is the first significant update since MultiPL-E was used in StarCoder 1.
 
-LANG = "lua"
-MODEL_NAME = "Salesforce/codegen-350M-multi"
-tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-model = AutoModelForCausalLM.from_pretrained(MODEL_NAME).half().cuda()
-problems = datasets.load_dataset("nuprl/MultiPL-E", f"humaneval-{LANG}")
+1. We no longer publish the MultiPL-E ablations, but they are available in
+   revision `d23b094` and earlier.
+2. New programming languages supported:
+   - Clojure, thanks to [Alex Miller](https://github.com/puredanger)
+   - Elixir, thanks to [Marko Vukovic](https://github.com/mvkvc)
+   - Haskell, thanks to https://github.com/Cajunvoodoo
+   - OCaml, thanks to [John Gouwar](https://johngouwar.github.io)
+3. Changes to existing HumanEval-based problems:
+   - Four Scala problems have fixed prompts/tests (12, 90, 128, 162).
+   - Some whitespace-only changes to problems for Racket (18 problems),
+     R (36 problems), Julia (159 problems), and D (156 problems). We will try to
+     avoid these kinds of changes in the future.
+1. The MBPP-based problems have changes analogous to the HumanEval-based problems.
 
-def stop_at_stop_token(decoded_string, problem):
-    """
-    Truncates the output at stop tokens, taking care to skip the prompt
-    which may have stop tokens.
-    """
-    min_stop_index = len(decoded_string)
-    for stop_token in problem["stop_tokens"]:
-        stop_index = decoded_string.find(stop_token)
-        if stop_index != -1 and stop_index > len(problem["prompt"]) and stop_index < min_stop_index:
-            min_stop_index = stop_index
-    return decoded_string[:min_stop_index]
-
-for problem in problems["test"]:
-    input_ids = tokenizer(
-        problem["prompt"],
-        return_tensors="pt",
-    ).input_ids.cuda()
-    generated_ids = model.generate(
-        input_ids, max_length=512, pad_token_id=tokenizer.eos_token_id + 2
-    )
-    truncated_string = stop_at_stop_token(tokenizer.decode(generated_ids[0]), problem)
-    filename = problem["name"] + "." + LANG
-    with open(filename, "w") as f:
-        print(f"Created {filename}")
-        f.write(truncated_string)
-        f.write("\n")
-        f.write(problem["tests"])
-```
+See the directory `diffs_v3.0` in the dataset repository for the diffs to
+each prompt.
 
 [BigCode Code Generation LM Harness]: https://github.com/bigcode-project/bigcode-evaluation-harness
 [MultiPL-E tutorial]: https://nuprl.github.io/MultiPL-E/

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