Spaces:
Runtime error
Runtime error
File size: 2,917 Bytes
a48f2db |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 |
import numpy as np
import transformers
from onnxruntime.quantization import quantize_dynamic,QuantType
import transformers.convert_graph_to_onnx as onnx_convert
from pathlib import Path
import os
import torch
from transformers import AutoModelForSequenceClassification,AutoTokenizer
chkpt='distilbert-base-uncased-finetuned-sst-2-english'
model=AutoModelForSequenceClassification.from_pretrained(chkpt)
tokenizer=AutoTokenizer.from_pretrained(chkpt)
# tokenizer=AutoTokenizer.from_pretrained('sentiment_classifier/')
def classify_sentiment(texts,model,tokenizer):
"""
user will pass texts separated by comma
"""
try:
texts=texts.split(',')
except:
pass
input = tokenizer(texts, padding=True, truncation=True,
return_tensors="pt")
logits = model(**input)['logits'].softmax(dim=1)
logits = torch.argmax(logits, dim=1)
output = ['Positive' if i == 1 else 'Negative' for i in logits]
return output
def create_onnx_model_sentiment(_model, _tokenizer):
"""
Args:
_model: model checkpoint with AutoModelForSequenceClassification
_tokenizer: model checkpoint with AutoTokenizer
Returns:
Creates a simple ONNX model & int8 Quantized Model in the directory "sent_clf_onnx/" if directory not present
"""
if not os.path.exists('sent_clf_onnx_dir'):
try:
os.mkdir('sent_clf_onnx_dir')
except:
pass
pipeline=transformers.pipeline("text-classification", model=_model, tokenizer=_tokenizer)
onnx_convert.convert_pytorch(pipeline,
opset=11,
output=Path("sent_clf_onnx_dir/sentiment_classifier_onnx.onnx"),
use_external_format=False
)
quantize_dynamic("sent_clf_onnx_dir/sentiment_classifier_onnx.onnx","sent_clf_onnx_dir/sentiment_classifier_onnx_quant.onnx",
weight_type=QuantType.QUInt8)
else:
pass
def classify_sentiment_onnx(texts, _session, _tokenizer):
"""
Args:
texts: input texts from user
_session: pass ONNX runtime session
_tokenizer: Relevant Tokenizer e.g. AutoTokenizer.from_pretrained("same checkpoint as the model")
Returns:
list of Positve and Negative texts
"""
try:
texts=texts.split(',')
except:
pass
_inputs = _tokenizer(texts, padding=True, truncation=True,
return_tensors="np")
input_feed={
"input_ids":np.array(_inputs['input_ids']),
"attention_mask":np.array((_inputs['attention_mask']))
}
output = _session.run(input_feed=input_feed, output_names=['output_0'])[0]
output=np.argmax(output,axis=1)
output = ['Positive' if i == 1 else 'Negative' for i in output]
return output
|