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import json |
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import uuid |
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from typing import Union |
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|
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import argilla as rg |
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import gradio as gr |
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import numpy as np |
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import pandas as pd |
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from datasets import ( |
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Dataset, |
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get_dataset_config_names, |
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get_dataset_split_names, |
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load_dataset, |
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) |
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from distilabel.distiset import Distiset |
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from gradio.oauth import OAuthToken |
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from gradio_huggingfacehub_search import HuggingfaceHubSearch |
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from huggingface_hub import HfApi, repo_exists |
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|
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from synthetic_dataset_generator.apps.base import ( |
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combine_datasets, |
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hide_success_message, |
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push_pipeline_code_to_hub, |
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show_success_message, |
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test_max_num_rows, |
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validate_argilla_user_workspace_dataset, |
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validate_push_to_hub, |
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) |
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from synthetic_dataset_generator.constants import DEFAULT_BATCH_SIZE |
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from synthetic_dataset_generator.pipelines.embeddings import ( |
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get_embeddings, |
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get_sentence_embedding_dimensions, |
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) |
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from synthetic_dataset_generator.pipelines.eval import ( |
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generate_pipeline_code, |
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get_custom_evaluator, |
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get_ultrafeedback_evaluator, |
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) |
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from synthetic_dataset_generator.utils import ( |
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column_to_list, |
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extract_column_names, |
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get_argilla_client, |
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get_org_dropdown, |
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pad_or_truncate_list, |
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process_columns, |
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swap_visibility, |
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) |
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|
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def get_iframe(hub_repo_id: str) -> str: |
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if not hub_repo_id: |
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return "" |
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|
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if not repo_exists(repo_id=hub_repo_id, repo_type="dataset"): |
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return "" |
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|
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url = f"https://huggingface.co./datasets/{hub_repo_id}/embed/viewer" |
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iframe = f""" |
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<iframe |
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src="{url}" |
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frameborder="0" |
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width="100%" |
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height="600px" |
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></iframe> |
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""" |
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return iframe |
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|
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def get_valid_columns(dataframe: pd.DataFrame): |
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instruction_valid_columns = [] |
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response_valid_columns = [] |
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|
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for col in dataframe.columns: |
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sample_val = dataframe[col].iloc[0] |
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if isinstance(sample_val, str) or ( |
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isinstance(sample_val, (list, np.ndarray)) |
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and all(isinstance(item, dict) and "role" in item for item in sample_val) |
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): |
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instruction_valid_columns.append(col) |
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response_valid_columns.append(col) |
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if isinstance(sample_val, (list, np.ndarray)) and all( |
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isinstance(item, str) for item in sample_val |
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): |
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response_valid_columns.append(col) |
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|
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return instruction_valid_columns, response_valid_columns |
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|
|
|
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def load_dataset_from_hub( |
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repo_id: str, num_rows: int = 10, token: Union[OAuthToken, None] = None |
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): |
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if not repo_id: |
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raise gr.Error("Hub repo id is required") |
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subsets = get_dataset_config_names(repo_id, token=token) |
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splits = get_dataset_split_names(repo_id, subsets[0], token=token) |
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ds = load_dataset(repo_id, subsets[0], split=splits[0], token=token, streaming=True) |
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rows = [] |
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for idx, row in enumerate(ds): |
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rows.append(row) |
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if idx == num_rows: |
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break |
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ds = Dataset.from_list(rows) |
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dataframe = ds.to_pandas() |
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instruction_valid_columns, response_valid_columns = get_valid_columns(dataframe) |
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col_instruction = instruction_valid_columns[0] if instruction_valid_columns else "" |
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col_response = "No valid response columns found." |
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for col in response_valid_columns: |
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if col != col_instruction: |
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col_response = col |
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break |
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|
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prompt_template = gr.Code( |
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label="Prompt template", |
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value="\n".join( |
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[ |
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"Evaluate the following text based on criteria.", |
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"Criteria: quality.", |
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"Score: between 1 and 10.", |
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"Text: {{" + col_response + "}}", |
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] |
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), |
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language="jinja2", |
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interactive=True, |
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) |
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structured_output = gr.Code( |
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label="Structured output", |
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value=json.dumps( |
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{ |
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"type": "object", |
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"properties": {"quality": {"type": "integer"}}, |
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"required": ["quality"], |
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}, |
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indent=4, |
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), |
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language="json", |
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interactive=True, |
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) |
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return ( |
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dataframe, |
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gr.Dropdown( |
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choices=instruction_valid_columns, |
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label="Instruction column", |
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value=col_instruction, |
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interactive=True, |
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), |
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gr.Dropdown( |
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choices=response_valid_columns, |
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label="Response column", |
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value=col_response, |
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interactive=False |
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if col_response == "No valid response columns found." |
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else True, |
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), |
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prompt_template, |
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structured_output, |
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) |
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|
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|
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def define_evaluation_aspects(task_type: str): |
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if task_type == "chat-eval": |
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return gr.Dropdown( |
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value=["overall-rating"], |
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choices=["helpfulness", "truthfulness", "overall-rating", "honesty"], |
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label="Evaluation Aspects", |
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multiselect=True, |
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interactive=True, |
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) |
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else: |
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return gr.Dropdown(interactive=False, visible=False) |
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|
|
|
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def evaluate_instruction_response( |
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dataframe: pd.DataFrame, |
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aspects: list[str], |
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instruction_column: str, |
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response_columns: str, |
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num_rows: int = 10, |
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is_sample: bool = False, |
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progress=gr.Progress(), |
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): |
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progress(0.0, desc="Evaluating instructions and responses") |
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data = process_columns(dataframe, instruction_column, response_columns) |
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num_generations = len(data[0]["generations"]) |
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evaluated_results = [] |
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for entry in data: |
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result_row = { |
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"instruction": entry["instruction"], |
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"generations": entry["generations"], |
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} |
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for aspect in aspects: |
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result_row[f"ratings_{aspect}"] = None |
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result_row[f"rationale_for_ratings_{aspect}"] = None |
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if aspect in ["truthfulness", "helpfulness"]: |
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result_row[f"type_{aspect}"] = None |
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result_row[f"rationale_for_type_{aspect}"] = None |
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result_row["model_name"] = None |
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evaluated_results.append(result_row) |
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|
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batch_size = DEFAULT_BATCH_SIZE |
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total_steps: int = len(aspects) * num_rows |
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|
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|
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for aspect in aspects: |
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ultrafeedback_evaluator = get_ultrafeedback_evaluator(aspect, is_sample) |
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n_processed = 0 |
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|
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while n_processed < num_rows: |
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progress( |
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(len(aspects) * n_processed) / total_steps, |
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total=total_steps, |
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desc=f"Evaluating aspect: {aspect}", |
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) |
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|
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remaining_rows = num_rows - n_processed |
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batch_size = min(batch_size, remaining_rows) |
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inputs = data[n_processed : n_processed + batch_size] |
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batch_results = list(ultrafeedback_evaluator.process(inputs=inputs)) |
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for j, result in enumerate(batch_results[0]): |
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idx = n_processed + j |
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evaluated_results[idx][f"ratings_{aspect}"] = pad_or_truncate_list( |
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result.get("ratings"), num_generations |
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) |
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evaluated_results[idx]["model_name"] = result.get("model_name") |
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if aspect in ["truthfulness", "helpfulness"]: |
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evaluated_results[idx][f"type_{aspect}"] = pad_or_truncate_list( |
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result.get("types"), num_generations |
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) |
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evaluated_results[idx][f"rationale_for_type_{aspect}"] = ( |
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pad_or_truncate_list(result.get("rationales"), num_generations) |
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) |
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evaluated_results[idx][f"rationale_for_ratings_{aspect}"] = ( |
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pad_or_truncate_list( |
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result.get("rationales-for-ratings"), num_generations |
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) |
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) |
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else: |
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evaluated_results[idx][f"rationale_for_ratings_{aspect}"] = ( |
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pad_or_truncate_list(result.get("rationales"), num_generations) |
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) |
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n_processed += batch_size |
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|
|
|
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dataframe = pd.DataFrame(evaluated_results) |
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progress(1.0, desc="Dataset evaluation completed") |
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return dataframe |
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|
|
|
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def evaluate_custom( |
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dataframe: pd.DataFrame, |
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prompt_template: str, |
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structured_output: dict, |
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num_rows: int = 10, |
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is_sample: bool = False, |
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progress=gr.Progress(), |
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): |
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progress(0.0, desc="Evaluating dataset") |
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columns = extract_column_names(prompt_template) |
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input_columns = {column: column_to_list(dataframe, column) for column in columns} |
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|
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custom_evaluator = get_custom_evaluator( |
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prompt_template, structured_output, columns, is_sample |
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) |
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batch_size = DEFAULT_BATCH_SIZE |
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|
|
|
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n_processed = 0 |
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evaluation_results = [] |
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while n_processed < num_rows: |
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progress( |
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n_processed / num_rows, |
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desc="Evaluating dataset", |
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) |
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remaining_rows = num_rows - n_processed |
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batch_size = min(batch_size, remaining_rows) |
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|
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inputs = [] |
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for idx in range(n_processed, n_processed + batch_size): |
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input = {column: input_columns[column][idx] for column in input_columns} |
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inputs.append(input) |
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|
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batch = list(custom_evaluator.process(inputs=inputs)) |
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evaluation_results.extend(batch[0]) |
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n_processed += batch_size |
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|
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|
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distiset_results = [] |
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for result in evaluation_results: |
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record = {key: result[key] for key in result if key != "distilabel_metadata"} |
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distiset_results.append(record) |
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|
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dataframe = pd.DataFrame(distiset_results) |
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progress(1.0, desc="Dataset evaluation completed") |
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return dataframe |
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|
|
|
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def _evaluate_dataset( |
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dataframe: pd.DataFrame, |
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eval_type: str, |
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aspects_instruction_response: list[str], |
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instruction_instruction_response: str, |
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response_instruction_response: str, |
|
prompt_template: str, |
|
structured_output: dict, |
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num_rows: int = 10, |
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is_sample: bool = False, |
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): |
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num_rows = test_max_num_rows(num_rows) |
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if eval_type == "chat-eval": |
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dataframe = evaluate_instruction_response( |
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dataframe=dataframe, |
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aspects=aspects_instruction_response, |
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instruction_column=instruction_instruction_response, |
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response_columns=response_instruction_response, |
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num_rows=num_rows, |
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is_sample=is_sample, |
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) |
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else: |
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dataframe = evaluate_custom( |
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dataframe=dataframe, |
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prompt_template=prompt_template, |
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structured_output=structured_output, |
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num_rows=num_rows, |
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is_sample=is_sample, |
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) |
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return dataframe |
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|
|
|
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def evaluate_sample_dataset( |
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repo_id: str, |
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eval_type: str, |
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aspects_instruction_response: list[str], |
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instruction_instruction_response: str, |
|
response_instruction_response: str, |
|
prompt_template: str, |
|
structured_output: dict, |
|
): |
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dataframe, _, _, _, _ = load_dataset_from_hub(repo_id, num_rows=10) |
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dataframe = _evaluate_dataset( |
|
dataframe=dataframe, |
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eval_type=eval_type, |
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aspects_instruction_response=aspects_instruction_response, |
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instruction_instruction_response=instruction_instruction_response, |
|
response_instruction_response=response_instruction_response, |
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prompt_template=prompt_template, |
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structured_output=structured_output, |
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num_rows=10, |
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is_sample=True, |
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) |
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return dataframe |
|
|
|
|
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def push_dataset_to_hub( |
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dataframe: pd.DataFrame, |
|
org_name: str, |
|
repo_name: str, |
|
oauth_token: Union[gr.OAuthToken, None], |
|
private: bool, |
|
pipeline_code: str, |
|
): |
|
repo_id = validate_push_to_hub(org_name, repo_name) |
|
dataset = Dataset.from_pandas(dataframe) |
|
dataset = combine_datasets(repo_id, dataset) |
|
distiset = Distiset({"default": dataset}) |
|
distiset.push_to_hub( |
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repo_id=repo_id, |
|
private=private, |
|
include_script=False, |
|
token=oauth_token.token, |
|
create_pr=False, |
|
) |
|
push_pipeline_code_to_hub(pipeline_code, org_name, repo_name, oauth_token) |
|
|
|
|
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def push_dataset( |
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org_name: str, |
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repo_name: str, |
|
private: bool, |
|
num_rows: int, |
|
original_repo_id: str, |
|
eval_type: str, |
|
aspects_instruction_response: list[str], |
|
instruction_instruction_response: str, |
|
response_instruction_response: str, |
|
prompt_template: str, |
|
structured_output: dict, |
|
pipeline_code: str, |
|
oauth_token: Union[gr.OAuthToken, None] = None, |
|
progress=gr.Progress(), |
|
) -> pd.DataFrame: |
|
dataframe, _, _, _, _ = load_dataset_from_hub(original_repo_id, num_rows=num_rows) |
|
dataframe = _evaluate_dataset( |
|
dataframe=dataframe, |
|
eval_type=eval_type, |
|
aspects_instruction_response=aspects_instruction_response, |
|
instruction_instruction_response=instruction_instruction_response, |
|
response_instruction_response=response_instruction_response, |
|
prompt_template=prompt_template, |
|
structured_output=structured_output, |
|
num_rows=num_rows, |
|
) |
|
push_dataset_to_hub( |
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dataframe, org_name, repo_name, oauth_token, private, pipeline_code |
|
) |
|
try: |
|
progress(0.1, desc="Setting up user and workspace") |
|
hf_user = HfApi().whoami(token=oauth_token.token)["name"] |
|
client = get_argilla_client() |
|
if client is None: |
|
return "" |
|
if eval_type == "chat-eval": |
|
num_generations = len((dataframe["generations"][0])) |
|
fields = [ |
|
rg.ChatField( |
|
name=f"chat_{i}", |
|
title=f"Chat {i+1}", |
|
description=f"User and assistant conversation for generation {i+1}", |
|
) |
|
for i in range(num_generations) |
|
] |
|
questions = [] |
|
for i in range(num_generations): |
|
for aspect in aspects_instruction_response: |
|
questions.append( |
|
rg.RatingQuestion( |
|
name=f"ratings_{aspect}_{i}", |
|
values=list(range(11)), |
|
title=f"Ratings for {aspect} for response {i+1}", |
|
required=True, |
|
) |
|
) |
|
questions.append( |
|
rg.TextQuestion( |
|
name=f"rationale_for_ratings_{aspect}_{i}", |
|
title=f"Rationale for ratings for {aspect} for response {i+1}", |
|
required=False, |
|
use_markdown=True, |
|
) |
|
) |
|
if aspect in ["truthfulness", "helpfulness"]: |
|
questions.append( |
|
rg.RatingQuestion( |
|
name=f"type_{aspect}_{i}", |
|
values=list(range(1, 6)), |
|
title=f"The type of the response {i+1} for {aspect}", |
|
required=True, |
|
) |
|
) |
|
questions.append( |
|
rg.TextQuestion( |
|
name=f"rationale_for_type_{aspect}_{i}", |
|
title=f"Rationale for type of the response {i+1} for {aspect}", |
|
required=False, |
|
use_markdown=True, |
|
) |
|
) |
|
metadata = [ |
|
rg.IntegerMetadataProperty( |
|
name="instruction_length", title="Instruction length" |
|
), |
|
] |
|
for i in range(num_generations): |
|
metadata.append( |
|
rg.IntegerMetadataProperty( |
|
name=f"response_{i}_length", title=f"Response {i+1} length" |
|
) |
|
) |
|
vectors = [ |
|
rg.VectorField( |
|
name="instruction_embeddings", |
|
dimensions=get_sentence_embedding_dimensions(), |
|
) |
|
] |
|
settings = rg.Settings( |
|
fields=fields, |
|
questions=questions, |
|
metadata=metadata, |
|
vectors=vectors, |
|
guidelines="Please review the conversation and provide an evaluation.", |
|
) |
|
|
|
dataframe["instruction_length"] = dataframe["instruction"].apply(len) |
|
for i in range(num_generations): |
|
dataframe[f"response_{i}_length"] = dataframe["generations"].apply( |
|
lambda gens: len(gens[i]) if i < len(gens) else 0 |
|
) |
|
dataframe["instruction_embeddings"] = get_embeddings( |
|
dataframe["instruction"].to_list() |
|
) |
|
|
|
progress(0.5, desc="Creating dataset") |
|
rg_dataset = client.datasets(name=repo_name, workspace=hf_user) |
|
if rg_dataset is None: |
|
rg_dataset = rg.Dataset( |
|
name=repo_name, |
|
workspace=hf_user, |
|
settings=settings, |
|
client=client, |
|
) |
|
rg_dataset = rg_dataset.create() |
|
|
|
progress(0.7, desc="Pushing dataset to Argilla") |
|
hf_dataset = Dataset.from_pandas(dataframe) |
|
records = [] |
|
for sample in hf_dataset: |
|
fields = {} |
|
metadata = {"instruction_length": sample.get("instruction_length", 0)} |
|
vectors = { |
|
"instruction_embeddings": sample.get("instruction_embeddings", []) |
|
} |
|
suggestions = [] |
|
generations = sample.get("generations", []) |
|
for i in range(num_generations): |
|
fields[f"chat_{i}"] = [ |
|
{"role": "user", "content": sample.get("instruction", "")}, |
|
{"role": "assistant", "content": generations[i]}, |
|
] |
|
metadata[f"response_{i}_length"] = sample.get( |
|
f"response_{i}_length", 0 |
|
) |
|
|
|
for aspect in aspects_instruction_response: |
|
ratings = sample.get(f"ratings_{aspect}", []) |
|
rationales = sample.get(f"rationale_for_ratings__{aspect}", []) |
|
|
|
rating_value = ( |
|
ratings[i] |
|
if ratings and isinstance(ratings[i], int) |
|
else None |
|
) |
|
rationale_value = ( |
|
rationales[i] |
|
if rationales and isinstance(rationales[i], str) |
|
else None |
|
) |
|
|
|
if rating_value is not None: |
|
suggestions.append( |
|
rg.Suggestion( |
|
question_name=f"ratings_{aspect}_{i}", |
|
value=rating_value, |
|
) |
|
) |
|
if rationale_value is not None: |
|
suggestions.append( |
|
rg.Suggestion( |
|
question_name=f"rationale_for_ratings_{aspect}_{i}", |
|
value=rationale_value, |
|
) |
|
) |
|
|
|
if aspect in ["truthfulness", "helpfulness"]: |
|
types = sample.get(f"type_{aspect}", []) |
|
rationale_types = sample.get( |
|
f"rationale_for_type_{aspect}", [] |
|
) |
|
|
|
type_value = ( |
|
types[i] |
|
if types and isinstance(types[i], int) |
|
else None |
|
) |
|
rationale_type_value = ( |
|
rationale_types[i] |
|
if rationale_types |
|
and isinstance(rationale_types[i], str) |
|
else None |
|
) |
|
if type_value is not None: |
|
suggestions.append( |
|
rg.Suggestion( |
|
question_name=f"type_{aspect}_{i}", |
|
value=type_value, |
|
) |
|
) |
|
if rationale_type_value is not None: |
|
suggestions.append( |
|
rg.Suggestion( |
|
question_name=f"rationale_for_type_{aspect}_{i}", |
|
value=rationale_type_value, |
|
) |
|
) |
|
records.append( |
|
rg.Record( |
|
fields=fields, |
|
metadata=metadata, |
|
vectors=vectors, |
|
suggestions=suggestions, |
|
) |
|
) |
|
rg_dataset.records.log(records=records) |
|
progress(1.0, desc="Dataset pushed to Argilla") |
|
else: |
|
columns = extract_column_names(prompt_template) |
|
settings = rg.Settings( |
|
fields=[ |
|
rg.TextField( |
|
name=column, |
|
title=column.capitalize(), |
|
description="The column content", |
|
) |
|
for column in columns |
|
], |
|
questions=[ |
|
rg.TextQuestion( |
|
name="evaluation", |
|
title="Evaluation", |
|
description="The generated evaluation", |
|
use_markdown=True, |
|
), |
|
], |
|
metadata=[ |
|
rg.IntegerMetadataProperty( |
|
name=f"{column}_length", title=f"{column.capitalize()} length" |
|
) |
|
for column in columns |
|
], |
|
vectors=[ |
|
rg.VectorField( |
|
name=f"{column}_embeddings", |
|
dimensions=get_sentence_embedding_dimensions(), |
|
) |
|
for column in columns |
|
], |
|
guidelines="Please review, correct and provide an accurate evaluation.", |
|
) |
|
for column in columns: |
|
dataframe[f"{column}_length"] = dataframe[column].apply(len) |
|
dataframe[f"{column}_embeddings"] = get_embeddings(dataframe[column]) |
|
|
|
progress(0.5, desc="Creating dataset") |
|
rg_dataset = client.datasets(name=repo_name, workspace=hf_user) |
|
if rg_dataset is None: |
|
rg_dataset = rg.Dataset( |
|
name=repo_name, |
|
workspace=hf_user, |
|
settings=settings, |
|
client=client, |
|
) |
|
rg_dataset = rg_dataset.create() |
|
progress(0.7, desc="Pushing dataset to Argilla") |
|
hf_dataset = Dataset.from_pandas(dataframe) |
|
rg_dataset.records.log( |
|
records=hf_dataset, mapping={"generation": "evaluation"} |
|
) |
|
progress(1.0, desc="Dataset pushed to Argilla") |
|
except Exception as e: |
|
raise gr.Error(f"Error pushing dataset to Argilla: {e}") |
|
return "" |
|
|
|
|
|
def show_pipeline_code_visibility(): |
|
return {pipeline_code_ui: gr.Accordion(visible=True)} |
|
|
|
|
|
def hide_pipeline_code_visibility(): |
|
return {pipeline_code_ui: gr.Accordion(visible=False)} |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
with gr.Blocks() as app: |
|
with gr.Column() as main_ui: |
|
gr.Markdown("## 1. Select your input dataset") |
|
with gr.Row(equal_height=False): |
|
with gr.Column(scale=2): |
|
search_in = HuggingfaceHubSearch( |
|
label="Search", |
|
placeholder="Search for a dataset", |
|
search_type="dataset", |
|
sumbit_on_select=True, |
|
) |
|
with gr.Row(): |
|
clear_btn_part = gr.Button("Clear", variant="secondary") |
|
load_btn = gr.Button("Load", variant="primary") |
|
|
|
with gr.Column(scale=3): |
|
examples = gr.Examples( |
|
examples=[ |
|
"argilla/distilabel-sft-easy", |
|
"HuggingFaceFW/fineweb-edu", |
|
"argilla/distilabel-intel-orca-dpo-pairs", |
|
], |
|
label="Example datasets", |
|
fn=lambda x: x, |
|
inputs=[search_in], |
|
run_on_click=True, |
|
) |
|
search_out = gr.HTML(label="Dataset preview", visible=False) |
|
|
|
gr.HTML(value="<hr>") |
|
gr.Markdown(value="## 2. Configure your task") |
|
with gr.Row(equal_height=False): |
|
with gr.Column(scale=2): |
|
eval_type = gr.Dropdown( |
|
label="Evaluation type", |
|
choices=["chat-eval", "custom-eval"], |
|
value="chat-eval", |
|
multiselect=False, |
|
visible=False, |
|
) |
|
with gr.Tab("Response Evaluation") as tab_instruction_response: |
|
aspects_instruction_response = define_evaluation_aspects( |
|
"chat-eval" |
|
) |
|
instruction_instruction_response = gr.Dropdown( |
|
label="Instruction Column", |
|
info="Select the instruction column to evaluate", |
|
choices=["Load your data first in step 1."], |
|
value="Load your data first in step 1.", |
|
interactive=False, |
|
multiselect=False, |
|
allow_custom_value=False, |
|
) |
|
response_instruction_response = gr.Dropdown( |
|
label="Response Column", |
|
info="Select the response column(s) to evaluate", |
|
choices=["Load your data first in step 1."], |
|
value="Load your data first in step 1.", |
|
interactive=False, |
|
multiselect=False, |
|
allow_custom_value=False, |
|
) |
|
tab_instruction_response.select( |
|
fn=lambda: "chat-eval", |
|
inputs=[], |
|
outputs=[eval_type], |
|
) |
|
with gr.Tab("Custom Evaluation Prompt") as tab_custom: |
|
aspects_custom = define_evaluation_aspects("custom-eval") |
|
prompt_template = gr.Code( |
|
label="Prompt template", |
|
value="Load your data first in step 1.", |
|
language="markdown", |
|
interactive=False, |
|
) |
|
structured_output = gr.Code( |
|
label="Structured output", |
|
value="Load your data first in step 1.", |
|
language="json", |
|
interactive=False, |
|
) |
|
tab_custom.select( |
|
fn=lambda: "custom-eval", |
|
inputs=[], |
|
outputs=[eval_type], |
|
) |
|
with gr.Row(): |
|
clear_btn_full = gr.Button("Clear", variant="secondary") |
|
btn_apply_to_sample_dataset = gr.Button("Save", variant="primary") |
|
with gr.Column(scale=3): |
|
dataframe = gr.Dataframe( |
|
headers=["prompt", "completion", "evaluation"], |
|
wrap=True, |
|
interactive=False, |
|
) |
|
|
|
gr.HTML(value="<hr>") |
|
gr.Markdown(value="## 3. Evaluate your dataset") |
|
with gr.Row(equal_height=False): |
|
with gr.Column(scale=2): |
|
org_name = get_org_dropdown() |
|
repo_name = gr.Textbox( |
|
label="Repo name", |
|
placeholder="dataset_name", |
|
value=f"my-distiset-{str(uuid.uuid4())[:8]}", |
|
interactive=True, |
|
) |
|
num_rows = gr.Number( |
|
label="Number of rows", |
|
value=10, |
|
interactive=True, |
|
scale=1, |
|
) |
|
private = gr.Checkbox( |
|
label="Private dataset", |
|
value=False, |
|
interactive=True, |
|
scale=1, |
|
) |
|
btn_push_to_hub = gr.Button("Push to Hub", variant="primary", scale=2) |
|
with gr.Column(scale=3): |
|
success_message = gr.Markdown( |
|
visible=True, |
|
min_height=100, |
|
) |
|
with gr.Accordion( |
|
"Customize your pipeline with distilabel", |
|
open=False, |
|
visible=False, |
|
) as pipeline_code_ui: |
|
code = generate_pipeline_code( |
|
repo_id=search_in.value, |
|
aspects=aspects_instruction_response.value, |
|
instruction_column=instruction_instruction_response, |
|
response_columns=response_instruction_response, |
|
prompt_template=prompt_template.value, |
|
structured_output=structured_output.value, |
|
num_rows=num_rows.value, |
|
eval_type=eval_type.value, |
|
) |
|
pipeline_code = gr.Code( |
|
value=code, |
|
language="python", |
|
label="Distilabel Pipeline Code", |
|
) |
|
|
|
search_in.submit(fn=get_iframe, inputs=search_in, outputs=search_out).then( |
|
fn=lambda df: pd.DataFrame(columns=df.columns), |
|
inputs=[dataframe], |
|
outputs=[dataframe], |
|
) |
|
|
|
load_btn.click( |
|
fn=load_dataset_from_hub, |
|
inputs=[search_in], |
|
outputs=[ |
|
dataframe, |
|
instruction_instruction_response, |
|
response_instruction_response, |
|
prompt_template, |
|
structured_output, |
|
], |
|
) |
|
|
|
btn_apply_to_sample_dataset.click( |
|
fn=evaluate_sample_dataset, |
|
inputs=[ |
|
search_in, |
|
eval_type, |
|
aspects_instruction_response, |
|
instruction_instruction_response, |
|
response_instruction_response, |
|
prompt_template, |
|
structured_output, |
|
], |
|
outputs=dataframe, |
|
) |
|
|
|
btn_push_to_hub.click( |
|
fn=validate_argilla_user_workspace_dataset, |
|
inputs=[repo_name], |
|
outputs=[success_message], |
|
show_progress=True, |
|
).then( |
|
fn=validate_push_to_hub, |
|
inputs=[org_name, repo_name], |
|
outputs=[success_message], |
|
show_progress=True, |
|
).success( |
|
fn=hide_success_message, |
|
outputs=[success_message], |
|
show_progress=True, |
|
).success( |
|
fn=hide_pipeline_code_visibility, |
|
inputs=[], |
|
outputs=[pipeline_code_ui], |
|
).success( |
|
fn=push_dataset, |
|
inputs=[ |
|
org_name, |
|
repo_name, |
|
private, |
|
num_rows, |
|
search_in, |
|
eval_type, |
|
aspects_instruction_response, |
|
instruction_instruction_response, |
|
response_instruction_response, |
|
prompt_template, |
|
structured_output, |
|
pipeline_code, |
|
], |
|
outputs=[success_message], |
|
show_progress=True, |
|
).success( |
|
fn=show_success_message, |
|
inputs=[org_name, repo_name], |
|
outputs=[success_message], |
|
).success( |
|
fn=generate_pipeline_code, |
|
inputs=[ |
|
search_in, |
|
prompt_template, |
|
structured_output, |
|
eval_type, |
|
], |
|
outputs=[pipeline_code], |
|
).success( |
|
fn=show_pipeline_code_visibility, |
|
inputs=[], |
|
outputs=[pipeline_code_ui], |
|
) |
|
|
|
clear_btn_part.click(fn=lambda x: "", inputs=[], outputs=[search_in]) |
|
clear_btn_full.click( |
|
fn=lambda df: ("", "", pd.DataFrame(columns=df.columns)), |
|
inputs=[dataframe], |
|
outputs=[ |
|
search_in, |
|
instruction_instruction_response, |
|
response_instruction_response, |
|
], |
|
) |
|
|
|
app.load(fn=swap_visibility, outputs=main_ui) |
|
app.load(fn=get_org_dropdown, outputs=[org_name]) |
|
|
