add readme updates

README.md

@@ -20,12 +20,12 @@ hf_oauth_scopes:
 
 <h1 align="center">
   <br>
-  🧬 Synthetic Data Generator
+  Synthetic Data Generator
   <br>
 </h1>
 <h3 align="center">Build datasets using natural language</h2>
 
-![Synthetic Data Generator](https://huggingface.co/spaces/argilla/synthetic-data-generator/resolve/main/assets/ui.png)
+![Synthetic Data Generator](https://huggingface.co/spaces/argilla/synthetic-data-generator/resolve/main/assets/ui-full.png)
 
 <p align="center">
 <a  href="https://pypi.org/project/synthetic-dataset-generator/">

src/distilabel_dataset_generator/pipelines/eval.py

@@ -1,10 +1,8 @@
-from typing import List
-
 from datasets import get_dataset_config_names, get_dataset_split_names
 from distilabel.llms import InferenceEndpointsLLM
 from distilabel.steps.tasks import (
-    UltraFeedback,
     TextGeneration,
+    UltraFeedback,
 )
 
 from src.distilabel_dataset_generator.pipelines.base import (
@@ -21,7 +19,7 @@ def get_ultrafeedback_evaluator(aspect, is_sample):
             tokenizer_id=MODEL,
             api_key=_get_next_api_key(),
             generation_kwargs={
-                "temperature": 0.7,
+                "temperature": 0,
                 "max_new_tokens": 256 if is_sample else 2048,
             },
         ),
@@ -39,12 +37,12 @@ def get_custom_evaluator(prompt_template, structured_output, columns, is_sample)
             api_key=_get_next_api_key(),
             structured_output={"format": "json", "schema": structured_output},
             generation_kwargs={
-                "temperature": 0.7,
+                "temperature": 0,
                 "max_new_tokens": 256 if is_sample else 2048,
             },
         ),
         template=prompt_template,
-        columns=columns
+        columns=columns,
     )
     custom_evaluator.load()
     return custom_evaluator
@@ -81,13 +79,13 @@ def generate_ultrafeedback_pipeline_code(
             tokenizer_id=MODEL,
             api_key=os.environ["HF_TOKEN"],
             generation_kwargs={{
-                "temperature": 0.7,
+                "temperature": 0,
                 "max_new_tokens": 2048,
             }},
         ),
         aspect=aspect,
     )
-    
+
     load_the_dataset >> ultrafeedback_evaluator
 
 if __name__ == "__main__":
@@ -113,7 +111,7 @@ def generate_ultrafeedback_pipeline_code(
     load_the_dataset = LoadDataFromDicts(
         data = data,
     )
-    
+
     tasks = []
     for aspect in aspects:
         evaluate_responses = UltraFeedback(
@@ -124,7 +122,7 @@ def generate_ultrafeedback_pipeline_code(
                 tokenizer_id=MODEL,
                 api_key=os.environ["HF_TOKEN"],
                 generation_kwargs={{
-                    "temperature": 0.7,
+                    "temperature": 0,
                     "max_new_tokens": 2048,
                 }},
             output_mappings={{
@@ -135,9 +133,9 @@ def generate_ultrafeedback_pipeline_code(
             }} if aspect in ["truthfulness", "helpfulness"] else {{"rationales": f"rationales_{{aspect}}", "ratings": f"ratings_{{aspect}}"}},
         )
         tasks.append(evaluate_responses)
-    
+
     combine_outputs = CombineOutputs()
-    
+
     load_the_dataset >> tasks >> combine_outputs
 
 if __name__ == "__main__":
@@ -177,14 +175,14 @@ def generate_custom_pipeline_code(
             api_key=os.environ["HF_TOKEN"],
             structured_output={{"format": "json", "schema": {structured_output}}},
             generation_kwargs={{
-                "temperature": 0.7,
+                "temperature": 0,
                 "max_new_tokens": 2048,
             }},
         ),
         template=CUSTOM_TEMPLATE,
         columns={columns}
     )
-    
+
     load_the_dataset >> custom_evaluator
 
 if __name__ == "__main__":
@@ -193,13 +191,32 @@ def generate_custom_pipeline_code(
     return code
 
 
-def generate_pipeline_code(repo_id, aspects, instruction_column, response_columns, prompt_template, structured_output, num_rows, eval_type):
+def generate_pipeline_code(
+    repo_id,
+    aspects,
+    instruction_column,
+    response_columns,
+    prompt_template,
+    structured_output,
+    num_rows,
+    eval_type,
+):
     if repo_id is None:
         subset = "default"
         split = "train"
     else:
         subset = get_dataset_config_names(repo_id)[0]
         split = get_dataset_split_names(repo_id, subset)[0]
     if eval_type == "ultrafeedback":
-        return generate_ultrafeedback_pipeline_code(repo_id, subset, split, aspects, instruction_column, response_columns, num_rows)
-    return generate_custom_pipeline_code(repo_id, subset, split, prompt_template, structured_output, num_rows)
+        return generate_ultrafeedback_pipeline_code(
+            repo_id,
+            subset,
+            split,
+            aspects,
+            instruction_column,
+            response_columns,
+            num_rows,
+        )
+    return generate_custom_pipeline_code(
+        repo_id, subset, split, prompt_template, structured_output, num_rows
+    )