ByteMeHarder-404/basic_sentimentanalysis_finetuning_sst2

BERT Base (uncased) fine-tuned on SST-2

This model is a fine-tuned version of bert-base-uncased on the GLUE SST-2 dataset for sentiment classification (positive vs. negative).

Model Details

• Model type: BERT (base, uncased)
• Fine-tuned on: SST-2 (Stanford Sentiment Treebank)
• Labels:
  • 0 → Negative
  • 1 → Positive
• Training framework: 🤗 Transformers

Training

• Epochs: 2
• Batch size: 4 (with gradient accumulation steps = 4)
• Learning rate: 3e-5
• Mixed precision: fp16
• Optimizer & Scheduler: Default Hugging Face Trainer

Evaluation Results

On the SST-2 validation set:

Epoch	Training Loss	Validation Loss	Accuracy
1	0.1761	0.2282	93.0%
2	0.1127	0.2701	93.1%

Final averaged training loss: 0.1663

How to Use

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_name = "ByteMeHarder-404/bert-base-uncased-finetuned-sst2"
tok = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

inputs = tok("I love Hugging Face!", return_tensors="pt")
outputs = model(**inputs)
pred = outputs.logits.argmax(dim=-1).item()
print("Label:", pred)  # 1 = Positive