# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("diwank/maptask-deberta-pair")
model = AutoModelForSequenceClassification.from_pretrained("diwank/maptask-deberta-pair")Quick Links
maptask-deberta-pair
Deberta-based Daily MapTask style dialog-act annotations classification model
Example
from simpletransformers.classification import (
ClassificationModel, ClassificationArgs
)
model = ClassificationModel("deberta", "diwank/maptask-deberta-pair")
predictions, raw_outputs = model.predict([["Say what is the meaning of life?", "I dont know"]])
convert_to_label = lambda n: ["acknowledge (0), align (1), check (2), clarify (3), explain (4), instruct (5), query_w (6), query_yn (7), ready (8), reply_n (9), reply_w (10), reply_y (11)".split(', ')[i] for i in n]
convert_to_label(predictions) # reply_n (9)
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="diwank/maptask-deberta-pair")