Include pipeline script

pipeline.py

@@ -0,0 +1,48 @@
+# Requirements: `pip install distilabel[hf-inference-endpoints]`
+import os
+import random
+from distilabel.llms import InferenceEndpointsLLM
+from distilabel.pipeline import Pipeline
+from distilabel.steps import LoadDataFromDicts, KeepColumns
+from distilabel.steps.tasks import GenerateTextClassificationData
+
+MODEL = "meta-llama/Llama-3.1-8B-Instruct"
+BASE_URL = ""
+TEXT_CLASSIFICATION_TASK = "None"
+os.environ["API_KEY"] = (
+    "hf_xxx"  # https://huggingface.co/settings/tokens/new?ownUserPermissions=repo.content.read&ownUserPermissions=repo.write&globalPermissions=inference.serverless.write&canReadGatedRepos=true&tokenType=fineGrained
+)
+
+with Pipeline(name="textcat") as pipeline:
+
+    task_generator = LoadDataFromDicts(data=[{"task": TEXT_CLASSIFICATION_TASK}])
+
+    textcat_generation = GenerateTextClassificationData(
+        llm=InferenceEndpointsLLM(
+            model_id=MODEL,
+            base_url=BASE_URL,
+            api_key=os.environ["API_KEY"],
+            generation_kwargs={
+                "temperature": 0.8,
+                "max_new_tokens": 2048,
+                "do_sample": True,
+                "top_k": 50,
+                "top_p": 0.95,
+            },
+        ),
+        seed=random.randint(0, 2**32 - 1),
+        difficulty='high school',
+        clarity=None,
+        num_generations=10,
+        output_mappings={"input_text": "text"},
+    )
+    
+    keep_columns = KeepColumns(
+        columns=["text", "label"],
+    )
+
+    # Connect steps in the pipeline
+    task_generator >> textcat_generation >> keep_columns
+
+    if __name__ == "__main__":
+        distiset = pipeline.run()