
- 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.")
```
{"language": ["cs"], "library_name": "transformers", "tags": ["news", "nlp", "czech"], "datasets": ["hynky/czech_news_dataset_v2"]}
transformers pytorch roberta text-classification news nlp czech cs dataset:hynky/czech_news_dataset_v2 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #news #nlp #czech #cs #dataset-hynky/czech_news_dataset_v2 #autotrain_compatible #endpoints_compatible #region-us 


- A model for predicting the source of news articles
## Usage:


## Usage:
TAGS
#transformers #pytorch #roberta #text-classification #news #nlp #czech #cs #dataset-hynky/czech_news_dataset_v2 #autotrain_compatible #endpoints_compatible #region-us 
 ## Usage:
TAGS
#transformers #pytorch #roberta #text-classification #news #nlp #czech #cs #dataset-hynky/czech_news_dataset_v2 #autotrain_compatible #endpoints_compatible #region-us 
## Usage: