Lucky0626/bert-base-uncased-imdb2-v01

---
language:
- en
license: apache-2.0
tags:
- text-classification
- sentiment-analysis
- bert
datasets:
- imdb
metrics:
- accuracy
- f1
base_model: bert-base-uncased
---

Model Card for bert-base-uncased fine-tuned on IMDB Sentiment Classification

Model Description

This model is a text classification model fine-tuned from the [translate:bert-base-uncased] base model.
It is trained on the IMDB movie review dataset to classify sentiment into two categories:

• 0: Negative
• 1: Positive

Training Data

The model was fine-tuned using the IMDB dataset consisting of movie reviews labeled with positive or negative sentiments.

Evaluation

• Dataset: IMDB test set
• Metrics:
  • Accuracy: 0.77
  • F1-score: 0.77

## Usage

---
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "Lucky0626/bert-base-uncased-imdb2-v01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "This movie was absolutely fantastic!"
inputs = tokenizer(text, return_tensors="pt", max_length=128, truncation=True)

with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.argmax(outputs.logits, dim=-1)

print("Predicted sentiment:", predictions.item())
---

Limitations

• The model is trained only on English movie reviews and might not perform well on other languages or domains.
• Sentiment classification is binary; neutral or mixed sentiments are not classified.

License

• The base model, [translate:bert-base-uncased], is licensed under Apache 2.0.
• The IMDB dataset is publicly available for research purposes.