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| import gradio as gr | |
| from datasets import concatenate_datasets | |
| from huggingface_hub import HfApi | |
| from huggingface_hub.errors import HFValidationError | |
| from requests.exceptions import HTTPError | |
| from transformer_ranker import Result | |
| from transformer_ranker.datacleaner import DatasetCleaner, TaskCategory | |
| from transformer_ranker.embedder import Embedder | |
| import math | |
| DISABLED_BUTTON_VARIANT = "huggingface" | |
| ENABLED_BUTTON_VARIANT = "primary" | |
| HEADLINE = """ | |
| <h1 align="center">TransformerRanker</h1> | |
| <p align="center" style="max-width: 560px; margin: auto;"> | |
| A very simple library that helps you find the best-suited language model for your NLP task. | |
| All you need to do is to select a dataset and a list of pre-trained language models (LMs) from the 🤗 HuggingFace Hub. | |
| TransformerRanker will quickly estimate which of these LMs will perform best on the given dataset! | |
| </p> | |
| <p align="center" style="font-weight: bold; margin-top: 20px; display: flex; justify-content: center; gap: 10px;"> | |
| <a href="https://github.com/flairNLP/transformer-ranker"> | |
| <img src="https://img.shields.io/github/stars/flairNLP/transformer-ranker?style=social&label=Repository" alt="GitHub Badge"> | |
| </a> | |
| <a href="https://pypi.org/project/transformer-ranker/"> | |
| <img src="https://img.shields.io/badge/Package-orange?style=flat&logo=python" alt="Package Badge"> | |
| </a> | |
| <a href="https://github.com/flairNLP/transformer-ranker/blob/main/examples/01-walkthrough.md"> | |
| <img src="https://img.shields.io/badge/Tutorials-blue?style=flat&logo=readthedocs&logoColor=white" alt="Tutorials Badge"> | |
| </a> | |
| <img src="https://img.shields.io/badge/license-MIT-green?style=flat" alt="License: MIT"> | |
| </p> | |
| <p align="center">Developed at <a href="https://www.informatik.hu-berlin.de/en/forschung-en/gebiete/ml-en/">Humboldt University of Berlin</a>.</p> | |
| """ | |
| FOOTER = """ | |
| **Note:** This demonstration currently runs on a CPU and is suited for smaller models only. | |
| **Developers:** [@plonerma](https://huggingface.co/plonerma) and [@lukasgarbas](https://huggingface.co/lukasgarbas). | |
| For feedback, suggestions, or contributions, reach out via GitHub or leave a message in the [discussions](https://huggingface.co/spaces/lukasgarbas/transformer-ranker/discussions). | |
| """ | |
| CSS = """ | |
| .gradio-container{max-width: 800px !important} | |
| a {color: #ff9d00;} | |
| @media (prefers-color-scheme: dark) { a {color: #be185d;} } | |
| """ | |
| hf_api = HfApi() | |
| def check_dataset_exists(dataset_name): | |
| """Update loading button if dataset can be found""" | |
| try: | |
| hf_api.dataset_info(dataset_name) | |
| return gr.update(interactive=True, variant=ENABLED_BUTTON_VARIANT) | |
| except (HTTPError, HFValidationError): | |
| return gr.update(value="Load dataset", interactive=False, variant=DISABLED_BUTTON_VARIANT) | |
| def check_dataset_is_loaded(dataset, text_column, label_column, task_category): | |
| if dataset and text_column != "-" and label_column != "-" and task_category != "-": | |
| return gr.update(interactive=True, variant=ENABLED_BUTTON_VARIANT) | |
| else: | |
| return gr.update(interactive=False, variant=DISABLED_BUTTON_VARIANT) | |
| def get_dataset_info(dataset): | |
| """Show information for dataset settings""" | |
| joined_dataset = concatenate_datasets(list(dataset.values())) | |
| datacleaner = DatasetCleaner() | |
| try: | |
| text_column = datacleaner._find_column(joined_dataset, "text column") | |
| except ValueError: | |
| gr.Warning("Text column can not be found. Select it in the dataset settings.") | |
| text_column = "-" | |
| try: | |
| label_column = datacleaner._find_column(joined_dataset, "label column") | |
| except ValueError: | |
| gr.Warning("Label column can not be found. Select it in the dataset settings.") | |
| label_column = "-" | |
| task_category = "-" | |
| if label_column != "-": | |
| try: | |
| # Find or set the task_category | |
| task_category = datacleaner._find_task_category(joined_dataset, label_column) | |
| except ValueError: | |
| gr.Warning( | |
| "Task category could not be determined. The dataset must support classification or regression tasks.", | |
| ) | |
| pass | |
| num_samples = len(joined_dataset) | |
| return ( | |
| gr.update( | |
| value=task_category, | |
| choices=[str(t) for t in TaskCategory], | |
| interactive=True, | |
| ), | |
| gr.update( | |
| value=text_column, choices=joined_dataset.column_names, interactive=True | |
| ), | |
| gr.update( | |
| value="-", choices=["-", *joined_dataset.column_names], interactive=True | |
| ), | |
| gr.update( | |
| value=label_column, choices=joined_dataset.column_names, interactive=True | |
| ), | |
| num_samples, | |
| ) | |
| def compute_ratio(num_samples_to_use, num_samples): | |
| if num_samples > 0: | |
| return num_samples_to_use / num_samples | |
| else: | |
| return 0.0 | |
| def ensure_one_lm_selected(checkbox_values, previous_values): | |
| if not any(checkbox_values): | |
| return previous_values | |
| return checkbox_values | |
| # Apply monkey patch to enable callbacks | |
| _old_embed = Embedder.embed | |
| def _new_embed(embedder, sentences, batch_size: int = 32, **kw): | |
| if embedder.tracker is not None: | |
| embedder.tracker.update_num_batches(math.ceil(len(sentences) / batch_size)) | |
| return _old_embed(embedder, sentences, batch_size=batch_size, **kw) | |
| Embedder.embed = _new_embed | |
| _old_embed_batch = Embedder.embed_batch | |
| def _new_embed_batch(embedder, *args, **kw): | |
| r = _old_embed_batch(embedder, *args, **kw) | |
| if embedder.tracker is not None: | |
| embedder.tracker.update_batch_complete() | |
| return r | |
| Embedder.embed_batch = _new_embed_batch | |
| _old_init = Embedder.__init__ | |
| def _new_init(embedder, *args, tracker=None, **kw): | |
| _old_init(embedder, *args, **kw) | |
| embedder.tracker = tracker | |
| Embedder.__init__ = _new_init | |
| class EmbeddingProgressTracker: | |
| def __init__(self, *, progress, model_names): | |
| self.model_names = model_names | |
| self.progress_bar = progress | |
| def total(self): | |
| return len(self.model_names) | |
| def __enter__(self): | |
| self.progress_bar = gr.Progress(track_tqdm=False) | |
| self.current_model = -1 | |
| self.batches_complete = 0 | |
| self.batches_total = None | |
| return self | |
| def __exit__(self, typ, value, tb): | |
| if typ is None: | |
| self.progress_bar(1.0, desc="Done") | |
| else: | |
| self.progress_bar(1.0, desc="Error") | |
| # Do not suppress any errors | |
| return False | |
| def update_num_batches(self, total): | |
| self.current_model += 1 | |
| self.batches_complete = 0 | |
| self.batches_total = total | |
| self.update_bar() | |
| def update_batch_complete(self): | |
| self.batches_complete += 1 | |
| self.update_bar() | |
| def update_bar(self): | |
| i = self.current_model | |
| description = f"Running {self.model_names[i]} ({i + 1} / {self.total})" | |
| progress = i / self.total | |
| if self.batches_total is not None: | |
| progress += (self.batches_complete / self.batches_total) / self.total | |
| self.progress_bar(progress=progress, desc=description) | |