from transformers import pipeline

# Load the model from the Hugging Face Model Hub
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    tokenizer = AutoTokenizer.from_pretrained("ihgn/similar-questions")
    model = AutoModelForSeq2SeqLM.from_pretrained("ihgn/similar-questions")
    model = pipeline("text2text-generation", model=model_name)

# Configure the generation parameters
    generation_config = {
        "max_length": 512,
        "num_beams": 1,
        "top_k": 50,
        "top_p": 0.92,
        "do_sample": True,
        "num_return_sequences": 1
    }

# Generate text using the configured parameters
    input_text= "Your input text goes here."
    input_ids = tokenizer.encode(input_text, return_tensors="pt")
    generated_ids = model(input_ids, **generation_config)
    generated_text = tokenizer.decode(generated_ids.squeeze(), skip_special_tokens=True)
# Print the generated text
    print(generated_text)
{"datasets": ["tapaco"], "metrics": ["bleu", "rouge", "ter"], "pipeline_tag": "text2text-generation"}
transformers pytorch t5 text2text-generation dataset:tapaco autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #dataset-tapaco #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 

from transformers import pipeline

# Load the model from the Hugging Face Model Hub
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    tokenizer = AutoTokenizer.from_pretrained("ihgn/similar-questions")
    model = AutoModelForSeq2SeqLM.from_pretrained("ihgn/similar-questions")
    model = pipeline("text2text-generation", model=model_name)

# Configure the generation parameters
    generation_config = {
        "max_length": 512,
        "num_beams": 1,
        "top_k": 50,
        "top_p": 0.92,
        "do_sample": True,
        "num_return_sequences": 1
    }

# Generate text using the configured parameters
    input_text= "Your input text goes here."
    input_ids = URL(input_text, return_tensors="pt")
    generated_ids = model(input_ids, generation_config)
    generated_text = URL(generated_ids.squeeze(), skip_special_tokens=True)
# Print the generated text
    print(generated_text)
# Load the model from the Hugging Face Model Hub
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    tokenizer = AutoTokenizer.from_pretrained("ihgn/similar-questions")
    model = AutoModelForSeq2SeqLM.from_pretrained("ihgn/similar-questions")
    model = pipeline("text2text-generation", model=model_name) # Configure the generation parameters
    generation_config = {
        "max_length": 512,
        "num_beams": 1,
        "top_k": 50,
        "top_p": 0.92,
        "do_sample": True,
        "num_return_sequences": 1
    } # Generate text using the configured parameters
    input_text= "Your input text goes here."
    input_ids = URL(input_text, return_tensors="pt")
    generated_ids = model(input_ids, generation_config)
    generated_text = URL(generated_ids.squeeze(), skip_special_tokens=True) # Print the generated text
    print(generated_text)
TAGS
#transformers #pytorch #t5 #text2text-generation #dataset-tapaco #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 # Load the model from the Hugging Face Model Hub
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    tokenizer = AutoTokenizer.from_pretrained("ihgn/similar-questions")
    model = AutoModelForSeq2SeqLM.from_pretrained("ihgn/similar-questions")
    model = pipeline("text2text-generation", model=model_name) # Configure the generation parameters
    generation_config = {
        "max_length": 512,
        "num_beams": 1,
        "top_k": 50,
        "top_p": 0.92,
        "do_sample": True,
        "num_return_sequences": 1
    } # Generate text using the configured parameters
    input_text= "Your input text goes here."
    input_ids = URL(input_text, return_tensors="pt")
    generated_ids = model(input_ids, generation_config)
    generated_text = URL(generated_ids.squeeze(), skip_special_tokens=True) # Print the generated text
    print(generated_text)
TAGS
#transformers #pytorch #t5 #text2text-generation #dataset-tapaco #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
# Load the model from the Hugging Face Model Hub
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    tokenizer = AutoTokenizer.from_pretrained("ihgn/similar-questions")
    model = AutoModelForSeq2SeqLM.from_pretrained("ihgn/similar-questions")
    model = pipeline("text2text-generation", model=model_name)# Configure the generation parameters
    generation_config = {
        "max_length": 512,
        "num_beams": 1,
        "top_k": 50,
        "top_p": 0.92,
        "do_sample": True,
        "num_return_sequences": 1
    }# Generate text using the configured parameters
    input_text= "Your input text goes here."
    input_ids = URL(input_text, return_tensors="pt")
    generated_ids = model(input_ids, generation_config)
    generated_text = URL(generated_ids.squeeze(), skip_special_tokens=True)# Print the generated text
    print(generated_text)