Instructions to use yjmsvma/flant5-large-sufficiency-classifier-nl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yjmsvma/flant5-large-sufficiency-classifier-nl with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("yjmsvma/flant5-large-sufficiency-classifier-nl") model = AutoModelForSeq2SeqLM.from_pretrained("yjmsvma/flant5-large-sufficiency-classifier-nl") - Notebooks
- Google Colab
- Kaggle
import requests
API_URL = "https://xjbtlnqjwtoasdoj.us-east4.gcp.endpoints.huggingface.cloud"
headers = {
"Accept" : "application/json",
"Content-Type": "application/json"
}
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
prefix = "Determine whether the context is sufficient to answer the question:"
question = "name the unit of mass that is used in the same measurement system as gray per second?"
context = "The gray per second and kilogram are both measurement units within the International System of Units, representing absorbed dose rate and mass, respectively."
input_ = f"""{prefix}
Question: {question}
Context: {context}"""
output = query({
"inputs": input_,
"parameters": {}
})
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