bert-uncased-wnli

Model Description

Fine-tuned BERT model for sentiment classification on SST-2 dataset

Base Model

• Base Model: google-bert/bert-base-uncased
• Task: text-classification
• Dataset: sst2

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("takedarn/bert-uncased-wnli")
model = AutoModelForSequenceClassification.from_pretrained("takedarn/bert-uncased-wnli")

text = "This movie is great!"
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)

with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_class = torch.argmax(predictions, dim=-1)
    
print(f"Predicted class: {predicted_class.item()}")

Training Details

This model was fine-tuned using the following configuration:

• Task: text-classification
• Dataset: sst2
• Base model: google-bert/bert-base-uncased

Citation

If you use this model, please cite:

@misc{bert_uncased_wnli,
  author = {Your Name},
  title = {bert-uncased-wnli},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/takedarn/bert-uncased-wnli}
}