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takedarn/bert-medium-sst2

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face
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bert-medium-sst2

Model Description

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

Base Model

Usage

python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("takedarn/bert-medium-sst2")
model = AutoModelForSequenceClassification.from_pretrained("takedarn/bert-medium-sst2")

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:

bibtex
@misc{bert_medium_sst2,
  author = {Your Name},
  title = {bert-medium-sst2},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/takedarn/bert-medium-sst2}
}