Find the best language model for your downstream task. Load a dataset, select models from the 🤗 Hub, and rank them by transferability.
Developed at Humboldt University of Berlin.
""" FOOTER = """ **Note:** CPU-only quick demo. **Built by:** @lukasgarbas & @plonerma **Questions?** Open a [GitHub issue](https://github.com/flairNLP/transformer-ranker/issues) 🔫. """ CSS = """ .gradio-container { max-width: 800px; margin: auto; } """ UNSET = "-" hf_api = HfApi() preprocessing = DatasetCleaner() def validate_dataset(dataset_name): """Enable if dataset exists on Hub.""" try: hf_api.dataset_info(dataset_name) # quick dataset info call return gr.update(interactive=True) except (HTTPError, HFValidationError): return gr.update(value="Load data", interactive=False) def preprocess_dataset(dataset): """Use data preprocessing to find text/label columns and task category.""" data = concatenate_datasets(list(dataset.values())) try: text_column = preprocessing._find_column(data, "text column") except ValueError: gr.Warning("Text column not auto-detected — select in settings.") text_column = UNSET try: label_column = preprocessing._find_column(data, "label column") except ValueError: gr.Warning("Label column not auto-detected — select in settings.") label_column = UNSET task_category = UNSET if label_column != UNSET: try: task_category = preprocessing._find_task_category(data, label_column) except ValueError: gr.Warning("Task category not auto-detected — framework supports classification, regression.") text_column = gr.update(value=text_column, choices=data.column_names, interactive=True) label_column = gr.update(value=label_column, choices=data.column_names, interactive=True) text_pair = gr.update(value=UNSET, choices=[UNSET, *data.column_names], interactive=True) task_category = gr.update(value=task_category, choices=[str(t) for t in TaskCategory], interactive=True) sample_size = len(data) return task_category, text_column, text_pair, label_column, sample_size """ return ( text_column, gr.update( value=task_category, choices=[str(t) for t in TaskCategory], interactive=True, ), gr.update( value=text_column, choices=data.column_names, interactive=True ), gr.update( value=UNSET, choices=[UNSET, *data.column_names], interactive=True ), gr.update( value=label_column, choices=data.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_dataset_is_loaded(dataset, text_column, label_column, task_category): if dataset and text_column != UNSET and label_column != UNSET and task_category != UNSET: return gr.update(interactive=True) else: return gr.update(interactive=False) 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 @property 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)