You can deploy this model and start earning money today!

St3w31/
BiLSTMSpamClassifier

Name: BiLSTMSpamClassifier
Rating: 2.5 (13 reviews)

Views

No views yet

BiLSTM Text Classifier

Simple BiLSTM model PyTorch trained for SPAM detection on SMS Spam Collection
(Almeida, Tiago and Jos Hidalgo. 2011. SMS Spam Collection.
UCI Machine Learning Repository. https://doi.org/10.24432/C5CC84).

Important Notes

The model returns logits as output; to obtain probabilities, apply torch.sigmoid.
The model uses the bert-base-uncased tokenizer only for tokenization (the encoder is NOT BERT).
Number of parameters: ~4.4M

Files

BiLSTMClassifier.safetensors: trained weights
BiLSTMClassifier.py: model definition
config.json: hyperparameters

Usage

python

import json
import torch
from transformers import BertTokenizer
from safetensors.torch import load_file
from BiLSTMClassifier import BiLSTMClassifier

with open("config.json") as f:
    cfg = json.load(f)

model = BiLSTMClassifier(**cfg)

state_dict = load_file("BiLSTMClassifier.safetensors")
model.load_state_dict(state_dict)
model.eval()

sample_text = "URGENT HIRING! Earn $500/day working from home. No experience needed."

tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
tokens = tokenizer(sample_text, return_tensors="pt")

logits = model(tokens["input_ids"])
prob = torch.sigmoid(logits)

St3w31/BiLSTMSpamClassifier

BiLSTM Text Classifier

Important Notes

Files

Usage

St3w31/
BiLSTMSpamClassifier