metadata
language: en
library_name: transformers
base_model: roberta-base
tags:
- text-classification
- rte
- parameter-efficient-fine-tuning
RoBERTa-base fine-tuned on GLUE RTE (lora)
This checkpoint is one learning experiment comparing Full Fine-Tuning, BitFit, adapters, and LoRA on GLUE Recognizing Textual Entailment (RTE). It uses seed 42 and selects the highest validation epoch.
| Metric | Value |
|---|---|
| Best validation accuracy | 0.7148 |
| Best epoch | 6 |
| Trainable parameters | 887,042 |
| Total parameters | 125,534,212 |
Method
lora. See the project README for the exact shared training configuration.
This is an educational experiment, not a benchmark-level performance claim.
Load
from peft import PeftModel
from transformers import AutoModelForSequenceClassification
base = AutoModelForSequenceClassification.from_pretrained("roberta-base", num_labels=2)
model = PeftModel.from_pretrained(base, "Anirudh7003/roberta-base-rte-lora")