Update README.md

README.md

@@ -1,4 +1,57 @@
-Accuracy:  0.7597
-Precision: 0.9223
-Recall:    0.7407
-F1 Score:  0.8216
+---
+library_name: transformers
+datasets:
+- vector-institute/newsmediabias-plus
+language:
+- en
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+base_model:
+- google-bert/bert-base-uncased
+pipeline_tag: text-classification
+---
+
+# BERT NMB+ (Disinformation Sequence Classification):
+
+Classifies 512 chunks of a news article as "Likely" or "Unlikely" biased/disinformation.
+
+Fine-tuned BERT ([bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased)) on the `headline`, `aritcle_text` and `text_label` fields in the [News Media Bias Plus Dataset](https://huggingface.co/datasets/vector-institute/newsmediabias-plus).
+
+**This model was trained with weighted sampling so that each batch contains 50% 'Likely' examples and 50% 'Unlikely' examples.** The same model trained without weighted sampling is here, and got slightly better taining eval metrics. However, this model preformed better when predictions were evaluated by gpt-4o as a judge.
+
+### Metics
+
+*Evaluated on a 0.1 random sample of the NMB+ dataset, unseen during training*
+
+- Accuracy:  0.7597
+- Precision: 0.9223
+- Recall:    0.7407
+- F1 Score:  0.8216
+
+## How to Use:
+
+*Keep in mind, this model was trained on full 512 token chunks (tends to over-predict Unlikely for standalone sentences). If you're planning on processing stand alone sentences, you may find better results with this NMB+ model, which was trained on biased headlines.* 
+
+```
+from transformers import pipeline
+
+classifier = pipeline("text-classification", model="maximuspowers/nmbp-bert-full-articles-balanced")
+result = classifier("He was a terrible politician.", top_k=2)
+```
+
+### Example Response:
+```
+[
+  {
+    'label': 'Likely',
+    'score': 0.9967995882034302
+  },
+  {
+    'label': 'Unlikely',
+    'score': 0.003200419945642352
+  }
+]
+```