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import streamlit as st
import torch
from transformers import BertTokenizer, BertModel
from huggingface_hub import hf_hub_url, cached_download
def get_cls_layer(repo_id="furrutiav/beto_coherence"):
config_file_url = hf_hub_url(repo_id, filename="cls_layer.torch")
value = cached_download(config_file_url)
return torch.load(value, map_location=torch.device('cpu'))
cls_layer = get_cls_layer()
beto_model = BertModel.from_pretrained("furrutiav/beto_coherence", revision="df96f50cfb1e3f7923912a25b1c3a865116fae4a")
beto_tokenizer = BertTokenizer.from_pretrained("furrutiav/beto_coherence", revision="df96f50cfb1e3f7923912a25b1c3a865116fae4a", do_lower_case=False)
e = beto_model.eval()
def preproccesing(Q, A, maxlen=60):
Q = " ".join(str(Q).replace("\n", " ").split())
A = " ".join(str(A).replace("\n", " ").split())
Q = Q if Q != "" else "nan"
A = A if A != "" else "nan"
tokens1 = beto_tokenizer.tokenize(Q)
tokens1 = ['[CLS]'] + tokens1 + ['[SEP]']
if len(tokens1) < maxlen:
tokens1 = tokens1 + ['[PAD]' for _ in range(maxlen - len(tokens1))]
else:
tokens1 = tokens1[:maxlen-1] + ['[SEP]']
tokens2 = beto_tokenizer.tokenize(A)
tokens2 = tokens2 + ['[SEP]']
if len(tokens2) < maxlen:
tokens2 = tokens2 + ['[PAD]' for _ in range(maxlen - len(tokens2))]
else:
tokens2 = tokens2[:maxlen-1] + ['[SEP]']
tokens = tokens1+tokens2
tokens_ids = beto_tokenizer.convert_tokens_to_ids(tokens)
tokens_ids_tensor = torch.tensor(tokens_ids)
attn_mask = (tokens_ids_tensor != 1).long()
return tokens_ids_tensor, attn_mask
def C1Classifier(Q, A, is_probs=True):
tokens_ids_tensor, attn_mask = preproccesing(Q, A)
cont_reps = beto_model(tokens_ids_tensor.unsqueeze(0), attention_mask = attn_mask.unsqueeze(0))
cls_rep = cont_reps.last_hidden_state[:, 0]
logits = cls_layer(cls_rep)
probs = torch.sigmoid(logits)
soft_probs = probs.argmax(1)
if is_probs:
return probs.detach().numpy()[0]
else:
return soft_probs.numpy()[0]
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