Models
Datasets
Spaces
Posts
Docs
Enterprise
Pricing
Log In
Sign Up

Collections

Discover the best community collections!

Collections trending this week

meta-llama/Llama-2-70b

Text Generation • Updated Apr 17, 2024 • 537

Output Format Answer 3, part three:

# Display character details print("Character Details:", character_details)

Output Format Answer 3, part two:

# Extract character names and details character_details = ner_model(article)

Output Format Answer 3, part one:

# Initialize a named entity recognition model from Hugging Face ner_model = pipeline("ner")

Exponentially Faster Language Modelling

Paper • 2311.10770 • Published Nov 15, 2023 • 118

Output Format Answer 2, part three:

# Display sentiment analysis result print("Sentiment Analysis Result:", sentiment_result)

Output Format Answer 2, part two:

# Analyze sentiment in the article sentiment_result = sentiment_model(article)

Output Format Answer 2, part one:

# Initialize a sentiment analysis model from Hugging Face sentiment_model = pipeline("sentiment-analysis")

Output Format Answer 1, part six:

# Display results print("Key Elements:", key_elements) print("Themes:", themes) print("Recommendations:", recommendations)

Output Format Answer 1, part five:

themes = results['themes'] # Create a basic recommendation system recommendations = ["Innovative idea 1", "Innovative idea 2", "Innovative idea 3"]

Previous
1
...
9,271
9,272
9,273
9,274
9,275
...
10,199
Next

Company

TOS Privacy About Jobs

Website

Models Datasets Spaces Pricing Docs