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import os
import pandas as pd
import numpy as np
import pickle
import torch
import tqdm
from transformers import BertTokenizer, BertForSequenceClassification, CamembertTokenizer, CamembertForSequenceClassification
from torch.utils.data import TensorDataset, DataLoader, SequentialSampler
def generate_dataloader(tokenizer, sentences, batch_size = 8, max_len = 512):
# Tokenize all of the sentences and map the tokens to thier word IDs.
input_ids_test = []
# For every sentence...
for sent in sentences:
# `encode` will:
# (1) Tokenize the sentence.
# (2) Prepend the `[CLS]` token to the start.
# (3) Append the `[SEP]` token to the end.
# (4) Map tokens to their IDs.
encoded_sent = tokenizer.encode(
sent, # Sentence to encode.
add_special_tokens = True, # Add '[CLS]' and '[SEP]'
# This function also supports truncation and conversion
# to pytorch tensors, but I need to do padding, so I
# can't use these features.
#max_length = max_len, # Truncate all sentences.
#return_tensors = 'pt', # Return pytorch tensors.
)
input_ids_test.append(encoded_sent)
# Pad our input tokens
padded_test = []
for i in input_ids_test:
if len(i) > max_len:
padded_test.extend([i[:max_len]])
else:
padded_test.extend([i + [0] * (max_len - len(i))])
input_ids_test = np.array(padded_test)
# Create attention masks
attention_masks = []
# Create a mask of 1s for each token followed by 0s for padding
for seq in input_ids_test:
seq_mask = [float(i>0) for i in seq]
attention_masks.append(seq_mask)
# Convert to tensors.
inputs = torch.tensor(input_ids_test)
masks = torch.tensor(attention_masks)
#set batch size
# Create the DataLoader.
data = TensorDataset(inputs, masks)
prediction_sampler = SequentialSampler(data)
return DataLoader(data, sampler=prediction_sampler, batch_size=batch_size)
def predict(model, dataloader, device):
# Put model in evaluation mode
model.eval()
# Tracking variables
predictions_test , true_labels = [], []
pred_labels_ = []
# Predict
for batch in dataloader:
# Add batch to GPU
batch = tuple(t.to(device) for t in batch)
# Unpack the inputs from the dataloader
b_input_ids, b_input_mask = batch
# Telling the model not to compute or store gradients, saving memory and
# speeding up prediction
with torch.no_grad():
# Forward pass, calculate logit predictions
outputs = model(b_input_ids, token_type_ids=None,
attention_mask=b_input_mask)
logits = outputs[0]
#print(logits)
# Move logits and labels to CPU ???
logits = logits.detach().cpu().numpy()
#print(logits)
# Store predictions and true labels
predictions_test.append(logits)
pred_labels = []
for i in range(len(predictions_test)):
# The predictions for this batch are a 2-column ndarray (one column for "0"
# and one column for "1"). Pick the label with the highest value and turn this
# in to a list of 0s and 1s.
pred_labels_i = np.argmax(predictions_test[i], axis=1).flatten()
pred_labels.append(pred_labels_i)
pred_labels_ += [item for sublist in pred_labels for item in sublist]
return pred_labels_
def text_folder_to_dataframe(path):
data = []
# id,tome,filename,nb_words,content,domain
for tome in sorted(os.listdir(path)):
for article in sorted(os.listdir(path + "/" + tome)):
filename = article[:-4]
id = tome + filename
if article[-4:] == ".txt":
with open(path + "/" + tome + "/" + article) as f:
content = f.read()
data.append([id, tome, filename, content, len(content.split(' '))])
return pd.DataFrame(data, columns=['id', 'tome', 'filename', 'content', 'nb_words'])
if __name__ == '__main__':
# If there's a GPU available...
if torch.cuda.is_available():
# Tell PyTorch to use the GPU.
device = torch.device("cuda")
gpu_name = "cuda"
print('There are %d GPU(s) available.' % torch.cuda.device_count())
print('We will use the GPU:', torch.cuda.get_device_name(0))
# for MacOS
elif torch.backends.mps.is_available() and torch.backends.mps.is_built():
device = torch.device("mps")
gpu_name = "mps"
print('We will use the GPU')
else:
device = torch.device("cpu")
gpu_name = "cpu"
print('No GPU available, using the CPU instead.')
#############
## Load data
print("* Load data")
path = "/Users/lmoncla/Documents/Data/Corpus/LGE/Text/"
df_LGE = text_folder_to_dataframe(path)
#df_LGE = pd.read_csv(path + "data/LGE_withContent.tsv", sep="\t")
data_LGE = df_LGE["content"].values
#df_LGE.head()
#df_LGE.shape
#############
## Load model
print("* Load model")
model_name = "bert-base-multilingual-cased"
#model_name = "camembert-base"
model_path = path + "models/model_" + model_name + "_s10000.pt"
if model_name == 'bert-base-multilingual-cased' :
print('Loading Bert Tokenizer...')
tokenizer = BertTokenizer.from_pretrained(model_name)
elif model_name == 'camembert-base':
print('Loading Camembert Tokenizer...')
tokenizer = CamembertTokenizer.from_pretrained(model_name)
data_loader = generate_dataloader(tokenizer, data_LGE)
model = BertForSequenceClassification.from_pretrained(model_path).to(gpu_name)
#############
## Predict
print("* Predict")
pred = predict(model, data_loader, device)
encoder_filename = "models/label_encoder.pkl"
with open(path+encoder_filename, 'rb') as file:
encoder = pickle.load(file)
p2 = list(encoder.inverse_transform(pred))
df_LGE['domain'] = p2
#############
## Save results
filepath = path + "results_LGE/metadata-withContent.csv"
print("* Save results: ", filepath)
df_LGE.to_csv(filepath, sep="\,")
df_LGE.drop(columns=['content'], inplace=True)
filepath = path + "results_LGE/metadata.csv"
print("* Save results: ", filepath)
df_LGE.to_csv(filepath, sep="\,")