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import torch
import tensorflow as tf
import numpy as np
from transformers import Text2TextGenerationPipeline
class DemoT5QAPipeline(Text2TextGenerationPipeline):
def _forward(self, model_inputs, **generate_kwargs):
if self.framework == "pt":
in_b, input_length = model_inputs["input_ids"].shape
elif self.framework == "tf":
in_b, input_length = tf.shape(model_inputs["input_ids"]).numpy()
self.check_inputs(
input_length,
generate_kwargs.get("min_length", self.model.config.min_length),
generate_kwargs.get("max_length", self.model.config.max_length),
)
outputs = self.model.generate(**model_inputs, **generate_kwargs, return_dict_in_generate=True, output_scores=True, max_new_tokens=75)
# Code from the parent class
output_ids = outputs.sequences
out_b = output_ids.shape[0]
if self.framework == "pt":
output_ids = output_ids.reshape(in_b, out_b // in_b, *output_ids.shape[1:])
elif self.framework == "tf":
output_ids = tf.reshape(output_ids, (in_b, out_b // in_b, *output_ids.shape[1:]))
output_sequences = outputs.sequences
output_scores = outputs.scores
return {"output_ids": output_ids, "output_sequences": output_sequences, "output_scores": output_scores}
def postprocess(self, model_outputs):
guess_text = super().postprocess(model_outputs)[0]['generated_text']
transition_scores = self.model.compute_transition_scores(model_outputs['output_sequences'], model_outputs['output_scores'], normalize_logits=True)
log_probs = np.round(np.exp(transition_scores.cpu().numpy()), 3)[0]
guess_prob = np.product(log_probs)
return {'guess': guess_text, 'confidence': guess_prob} |