Instructions to use cvelist/spidder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cvelist/spidder with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("cvelist/spidder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 432 Bytes
467b421 3b7d5e3 467b421 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | import re
def preprocess_text(text):
# Remove special characters and digits
text = re.sub(r'\W', ' ', text)
text = re.sub(r'\s+', ' ', text)
return text.strip()
with open("data.csv", "r") as file:
data = file.readlines()
cleaned_data = [preprocess_text(line) for line in data]
# Save the cleaned data
with open("cleaned_data.txt", "w") as file:
for entry in cleaned_data:
file.write(entry + "\n") |