# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Anirudh7003/roberta-base-rte-adapter", device_map="auto")Quick Links
RoBERTa-base fine-tuned on GLUE RTE (adapter)
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 | 0.5271 |
| Best epoch | 3 |
| Trainable parameters | 1,191,554 |
| Total parameters | 125,246,594 |
Method
adapter. See the project README for the exact shared training configuration.
This is an educational experiment, not a benchmark-level performance claim.
Load
# See load_adapter.py in this repository.
from load_adapter import load_model
model, tokenizer = load_model(".")
Model tree for Anirudh7003/roberta-base-rte-adapter
Base model
FacebookAI/roberta-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Anirudh7003/roberta-base-rte-adapter")