# Initialize the pipeline with the "text-davinci-002" model
segmenter = pipeline("text-segmentation", model="text-davinci-002", tokenizer='text-davinci-002')

# Segment the text
segmented_text = segmenter("This is a longer text that we want to segment into smaller chunks. Each chunk should correspond to a coherent piece of text.")

# Process each segment with ChatGPT
nlp = pipeline("text-generation", model="text-davinci-002", tokenizer='text-davinci-002')
for segment in segmented_text:
    print(nlp(segment))

{"license": "openrail"}
license:openrail region:us
TAGS
#license-openrail #region-us 

# Initialize the pipeline with the "text-davinci-002" model
segmenter = pipeline("text-segmentation", model="text-davinci-002", tokenizer='text-davinci-002')

# Segment the text
segmented_text = segmenter("This is a longer text that we want to segment into smaller chunks. Each chunk should correspond to a coherent piece of text.")

# Process each segment with ChatGPT
nlp = pipeline("text-generation", model="text-davinci-002", tokenizer='text-davinci-002')
for segment in segmented_text:
    print(nlp(segment))

# Initialize the pipeline with the "text-davinci-002" model
segmenter = pipeline("text-segmentation", model="text-davinci-002", tokenizer='text-davinci-002') # Segment the text
segmented_text = segmenter("This is a longer text that we want to segment into smaller chunks. Each chunk should correspond to a coherent piece of text.") # Process each segment with ChatGPT
nlp = pipeline("text-generation", model="text-davinci-002", tokenizer='text-davinci-002')
for segment in segmented_text:
    print(nlp(segment))
TAGS
#license-openrail #region-us 
 # Initialize the pipeline with the "text-davinci-002" model
segmenter = pipeline("text-segmentation", model="text-davinci-002", tokenizer='text-davinci-002') # Segment the text
segmented_text = segmenter("This is a longer text that we want to segment into smaller chunks. Each chunk should correspond to a coherent piece of text.") # Process each segment with ChatGPT
nlp = pipeline("text-generation", model="text-davinci-002", tokenizer='text-davinci-002')
for segment in segmented_text:
    print(nlp(segment))
TAGS
#license-openrail #region-us 
# Initialize the pipeline with the "text-davinci-002" model
segmenter = pipeline("text-segmentation", model="text-davinci-002", tokenizer='text-davinci-002')# Segment the text
segmented_text = segmenter("This is a longer text that we want to segment into smaller chunks. Each chunk should correspond to a coherent piece of text.")# Process each segment with ChatGPT
nlp = pipeline("text-generation", model="text-davinci-002", tokenizer='text-davinci-002')
for segment in segmented_text:
    print(nlp(segment))