metadata
license: mit
How to load the model and generate predictions?
import torch
import transformers
from torch.utils.data import Dataset, DataLoader
from transformers import RobertaModel, RobertaTokenizer, BertModel, BertTokenizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
MAX_LEN = 128
BATCH_SIZE = 20
text_col_name = 'sentence'
category_col = 'label_text'
#Input should be one dataframe having one column with header as 'sentence' : test_df (do reset_index() if needed)
test_df = pd.DataFrame({"sentence":['a general increase in prices and fall in the purchasing value of money.']})
def scoring_data_prep(dataset):
out = []
target = []
mask = []
for i in range(len(dataset)):
rec = dataset[i]
out.append(rec['ids'].reshape(-1,MAX_LEN))
mask.append(rec['mask'].reshape(-1,MAX_LEN))
out_stack = torch.cat(out, dim = 0)
mask_stack = torch.cat(mask, dim =0 )
out_stack = out_stack.to(device, dtype = torch.long)
mask_stack = mask_stack.to(device, dtype = torch.long)
return out_stack, mask_stack
class Triage(Dataset):
"""
This is a subclass of torch packages Dataset class. It processes input to create ids, masks and targets required for model training.
"""
def __init__(self, dataframe, tokenizer, max_len, text_col_name, category_col):
self.len = len(dataframe)
self.data = dataframe
self.tokenizer = tokenizer
self.max_len = max_len
self.text_col_name = text_col_name
self.category_col = category_col
def __getitem__(self, index):
title = str(self.data[self.text_col_name][index])
title = " ".join(title.split())
inputs = self.tokenizer.encode_plus(
title,
None,
add_special_tokens=True,
max_length=self.max_len,
pad_to_max_length=True,
return_token_type_ids=True,
truncation=True,
)
ids = inputs["input_ids"]
mask = inputs["attention_mask"]
return {
"ids": torch.tensor(ids, dtype=torch.long),
"mask": torch.tensor(mask, dtype=torch.long),
"targets": torch.tensor(
self.data[self.category_col][index], dtype=torch.long
),
}
def __len__(self):
return self.len
class BERTClass(torch.nn.Module):
def __init__(self, num_class):
super(BERTClass, self).__init__()
self.num_class = num_class
self.l1 = BertModel.from_pretrained("ProsusAI/finbert")
self.pre_classifier = torch.nn.Linear(768, 768)
self.dropout = torch.nn.Dropout(0.3)
self.classifier = torch.nn.Linear(768, self.num_class)
self.history = dict()
def forward(self, input_ids, attention_mask):
output_1 = self.l1(input_ids=input_ids, attention_mask=attention_mask)
hidden_state = output_1[0]
pooler = hidden_state[:, 0]
pooler = self.pre_classifier(pooler)
pooler = torch.nn.ReLU()(pooler)
pooler = self.dropout(pooler)
output = self.classifier(pooler)
return output
def do_predict(tokenizer):
test_set = Triage(test_df, tokenizer, MAX_LEN, text_col_name)
test_params = {'batch_size' : BATCH_SIZE, 'shuffle': False, 'num_workers':0}
test_loader = DataLoader(test_set, **test_params)
out_stack, mask_stack = scoring_data_prep(dataset = test_set)
n = 0
combined_output = []
model.eval()
with torch.no_grad():
while n < test_df.shape[0]:
output = model(out_stack[n:n+BATCH_SIZE,:],mask_stack[n:n+BATCH_SIZE,:])
n = n + BATCH_SIZE
combined_output.append(output)
combined_output = torch.cat(combined_output, dim = 0)
preds = torch.argsort(combined_output, axis = 1, descending = True)
preds = preds.to('cpu')
actual_predictions = [i[0] for i in preds.tolist()]
return actual_predictions
model_read = BERTClass(2)
model_read.load_stat_dict(torch.load('pytorch_model.bin')['model_state_dict'])
tokenizer_read = BertTokenizer.from_pretrained('ProsusAI/finbert')
actual_predictions_read = do_predict(tokenizer_read)
test_df['readability'] = ['readable' if i==1 else 'not_reabale' for i in actual_predictions_read]
@InProceedings{ghosh-EtAl:2022:FNP,
author = {Ghosh, Sohom and Sengupta, Shovon and Naskar, Sudip and Singh, Sunny Kumar},
title = {FinRAD: Financial Readability Assessment Dataset - 13,000+ Definitions of Financial Terms for Measuring Readability},
booktitle = {Proceedings of the The 4th Financial Narrative Processing Workshop @LREC2022},
month = {June},
year = {2022},
address = {Marseille, France},
publisher = {European Language Resources Association},
pages = {1--9},
url = {http://www.lrec-conf.org/proceedings/lrec2022/workshops/FNP/pdf/2022.fnp-1.1.pdf}
}
``bibtex @InProceedings{ghosh-2021-finread, title = "FinRead: A Transfer Learning Based Tool to Assess Readability of Definitions of Financial Terms", author = "Sohom Ghosh, Shovon Sengupta, Sudip Kumar Naskar, Sunny Kumar Singh", booktitle = "Proceedings of the 18th International Conference on Natural Language Processing (ICON) : System Demonstrations", month = "dec", year = "2021", publisher = "NLP Association of India (NLPAI)", url = "forthcoming", intype = {to appear in}, pre-print = "https://easychair.org/publications/preprint/1wvS" } ```