Instructions to use hynky/Server with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hynky/Server with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hynky/Server")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hynky/Server") model = AutoModelForSequenceClassification.from_pretrained("hynky/Server", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| datasets: | |
| - hynky/czech_news_dataset_v2 | |
| language: | |
| - cs | |
| library_name: transformers | |
| tags: | |
| - news | |
| - nlp | |
| - czech | |
| - A model for predicting the source of news articles | |
| ## Usage: | |
| ``` | |
| import re | |
| from transformers import pipeline | |
| from html import unescape | |
| from unicodedata import normalize | |
| re_multispace = re.compile(r"\s+") | |
| def normalize_text(text): | |
| if text == None: | |
| return None | |
| text = text.strip() | |
| text = text.replace("\n", " ") | |
| text = text.replace("\t", " ") | |
| text = text.replace("\r", " ") | |
| text = re_multispace.sub(" ", text) | |
| text = unescape(text) | |
| text = normalize("NFKC", text) | |
| return text | |
| model = pipeline(task="text-classification", | |
| model=f"hynky/Server", tokenizer="ufal/robeczech-base", | |
| truncation=True, max_length=512, | |
| top_k=5 | |
| ) | |
| def predict(article): | |
| article = normalize_text(article) | |
| predictions = model(article) | |
| predict("Dnes v noci bude pršet.") | |
| ``` |