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import pandas as pd
from tqdm.auto import tqdm
import torch
from transformers import BertTokenizerFast as BertTokenizer, BertForSequenceClassification
import os
import glob
RANDOM_SEED = 42
pd.RANDOM_SEED = 42
LABEL_COLUMNS = ["Assertive Tone", "Conversational Tone", "Emotional Tone", "Informative Tone", "None"]
@torch.no_grad()
def predict_csv(data, text_col, tokenizer, model, device, text_bs=16, max_token_len=128):
predictions = []
post = data[text_col]
num_text = len(post)
generator = range(0, num_text, text_bs)
for i in tqdm(generator, total=len(generator), desc="Processing..."):
texts = post[i: min(num_text, i+text_bs)].tolist()
encoding = tokenizer(
texts,
add_special_tokens=True,
max_length=max_token_len,
return_token_type_ids=False,
padding="max_length",
truncation=True,
return_attention_mask=True,
return_tensors='pt',
)
logits = model(
encoding["input_ids"].to(device),
encoding["attention_mask"].to(device),
return_dict=True
).logits
prediction = torch.softmax(logits, dim=1)
predictions.append(prediction.detach().cpu())
final_pred = torch.cat(predictions, dim=0)
y_inten = final_pred.numpy().T
for i in range(len(LABEL_COLUMNS)):
data[LABEL_COLUMNS[i]] = [round(i, 8) for i in y_inten[i].tolist()]
return data
@torch.no_grad()
def predict_single(sentence, tokenizer, model, device, max_token_len=128):
encoding = tokenizer(
sentence,
add_special_tokens=True,
max_length=max_token_len,
return_token_type_ids=False,
padding="max_length",
truncation=True,
return_attention_mask=True,
return_tensors='pt',
)
logits = model(
encoding["input_ids"].to(device),
encoding["attention_mask"].to(device),
return_dict=True
).logits
prediction = torch.softmax(logits, dim=1)
y_inten = prediction.flatten().cpu().numpy().T.tolist()
y_inten = [round(i, 8) for i in y_inten]
return y_inten
if __name__ == "__main__":
Data = pd.read_csv("assets/Kickstarter_sentence_level_5000.csv")
Data = Data[:20]
device = torch.device('cpu')
# Load model directly
tokenizer = BertTokenizer.from_pretrained("Oliver12315/Brand_Tone_of_Voice")
model = BertForSequenceClassification.from_pretrained("Oliver12315/Brand_Tone_of_Voice")
model = model.to(device)
fk_doc_result = predict_csv(Data,"content", tokenizer, model, device)
single_response = predict_single("Games of the imagination teach us actions have consequences in a realm that can be reset.", tokenizer, model, device)
fk_doc_result.to_csv(f"output/prediction_Brand_Tone_of_Voice.csv") |