Jeopardy bot! (#17)

* support for jeopardy dataset

* commit the final config for jeopardy bot

src/axolotl/prompt_tokenizers.py

@@ -89,6 +89,15 @@ class AlpacaPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
         )
 
 
+class JeopardyPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
+    def parse_instruction_fields(self, prompt) -> (str, str, str):
+        return (
+            prompt["question"],
+            prompt["category"],
+            "what is " + prompt["answer"],
+        )
+
+
 class OpenAssistantPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
     def parse_instruction_fields(self, prompt) -> (str, str, str):
         return (

src/axolotl/prompters.py

@@ -31,6 +31,10 @@ class AlpacaPrompter:
         return output.split(self.response_split)[1].strip()
 
 
+class JeopardyPrompter(AlpacaPrompter):
+    prompt_input = "Below is a Jeopardy clue paired with input providing the category of the clue. Write a concise response that best answers tbe clue given the category.\n\n### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
+
+
 class GPTeacherPrompter(AlpacaPrompter):
     ...
 

src/axolotl/utils/data.py

@@ -11,13 +11,13 @@ from axolotl.prompt_tokenizers import (
     GPTeacherPromptTokenizingStrategy,
     OpenAssistantPromptTokenizingStrategy,
     AlpacaReflectionPTStrategy,
-    ShareGPTPromptTokenizingStrategy,
+    ShareGPTPromptTokenizingStrategy, JeopardyPromptTokenizingStrategy,
 )
 from axolotl.prompters import (
     AlpacaPrompter,
     GPTeacherPrompter,
     ReflectAlpacaPrompter,
-    ShareGPTPrompter,
+    ShareGPTPrompter, JeopardyPrompter,
 )
 
 
@@ -82,6 +82,12 @@ def load_prepare_datasets(tokenizer, cfg, default_dataset_prepared_path):
                 )
                 ds_wrapper = TokenizedPromptDataset(ds_strategy, ds["train"])
                 datasets.append(ds_wrapper)
+            if d.type == "jeopardy":
+                ds_strategy = JeopardyPromptTokenizingStrategy(
+                    JeopardyPrompter(), tokenizer, cfg.train_on_inputs, cfg.sequence_len
+                )
+                ds_wrapper = TokenizedPromptDataset(ds_strategy, ds["train"])
+                datasets.append(ds_wrapper)
             elif d.type == "oasst":
                 ds_strategy = OpenAssistantPromptTokenizingStrategy(
                     AlpacaPrompter(), tokenizer, cfg.train_on_inputs, cfg.sequence_len