Upload XLM-R-Model Checkpoints.zip

XLM-RoBERTa (Text-Only) Fine-Tuned on EMC

This model is a fine-tuned version of xlm-roberta-base trained on the Engineering Misinformation Corpus (EMC).
It was optimized for binary classification:

Class 0: Real engineering documents

Class 1: AI-generated misinformation

Unlike the fusion models, this checkpoint uses only raw text inputs (no engineered features).
It serves as a strong baseline for end-to-end Transformer performance on technical misinformation detection.

Training Details

Base model: xlm-roberta-base

Objective: Binary classification (cross-entropy)

Sequence length: 256 tokens

Optimizer: AdamW

Datasets: EMC Dataset
(train/val/test splits provided)

Intended Uses

Detecting AI-generated misinformation in engineering and technical texts.

Benchmarking Transformer-only models against feature-based or hybrid models.

⚠️ Limitations:

Trained only on engineering-domain corpora.

Performance may degrade on general-domain misinformation tasks.

Does not incorporate numerical or structural features (handled in fusion models).

XLM-R-Model Checkpoints.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fef54448714ac0f9c14a0630fd9a1107515f90ffbc399baf09a4fbd7ec2f9c4f
+size 821595019