File size: 2,939 Bytes
d9b3b55 0cd2c97 3a9c126 0cd2c97 3a9c126 0cd2c97 499c31b 0cd2c97 499c31b 0cd2c97 499c31b 0cd2c97 d9b3b55 |
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 |
import gradio as gr
from models import *
from huggingface_hub import hf_hub_download
import os
from config import *
device = torch.device('cuda' if torch.cuda.is_available() else "cpu")
ENTITY_REPO_ID = 'vaivTA/absa_v2_entity'
ENTITY_FILENAME = "entity_model.pt"
SENTIMENT_REPO_ID = 'vaivTA/absa_v2_sentiment'
SENTIMENT_FILENAME = "sentiment_model.pt"
print("downloading model...")
sen_model_file = hf_hub_download(repo_id=SENTIMENT_REPO_ID, filename=SENTIMENT_FILENAME)
entity_model_file = hf_hub_download(repo_id=ENTITY_REPO_ID, filename=ENTITY_FILENAME)
base_model = base_model
tokenizer = AutoTokenizer.from_pretrained(base_model)
sen_model = Classifier(base_model, num_labels=2, device=device, tokenizer=tokenizer)
sen_model.load_state_dict(torch.load('/resolve/main/'+sen_model_file))
entity_model = Classifier(base_model, num_labels=2, device=device, tokenizer=tokenizer)
entity_model.load_state_dict(torch.load(entity_model_file))
def infer(test_sentence):
entity_model.to(device)
entity_model.eval()
sen_model.to(device)
sen_model.eval()
form = test_sentence
annotation = []
if len(form) > 500:
return "Too long sentence!"
for pair in entity_property_pair:
form_ = form + "[SEP]"
pair_ = entity2str[pair] + "[SEP]"
tokenized_data = tokenizer(form_, pair_, padding='max_length', max_length=512, truncation=True)
input_ids = torch.tensor([tokenized_data['input_ids']]).to(device)
attention_mask = torch.tensor([tokenized_data['attention_mask']]).to(device)
first_sep = tokenized_data['input_ids'].index(2)
last_sep = tokenized_data['input_ids'][first_sep+2:].index(2) + (first_sep + 2)
mask = [0] * len(tokenized_data['input_ids'])
for i in range(first_sep + 2, last_sep):
mask[i] = 1
mask = torch.tensor([mask]).to(device)
with torch.no_grad():
outputs = entity_model(input_ids, attention_mask, mask)
ce_logits = outputs
ce_predictions = torch.argmax(ce_logits, dim = -1)
ce_result = tf_id_to_name[ce_predictions[0]]
if ce_result == 'True':
with torch.no_grad():
outputs = sen_model(input_ids, attention_mask, mask)
pc_logits = outputs
pc_predictions = torch.argmax(pc_logits, dim=-1)
pc_result = polarity_id_to_name[pc_predictions[0]]
annotation.append(f"{pair} - {pc_result}")
result = '\n'.join(annotation)
return result
article = "**๋ถ์ํ ํ
์คํธ๋ฅผ ์
๋ ฅํ์ธ์.**" \
demo = gr.Interface(fn=infer,
inputs=gr.Textbox(type="text", label="Input Sentence"),
outputs=gr.Textbox(type="text", label="Result Sentence")
# examples=[image_path,]
)
demo.launch(share=True)
|