Update README.md

README.md

@@ -1,3 +1,37 @@
 ---
 license: apache-2.0
 ---
+
+model base: https://huggingface.co/microsoft/mdeberta-v3-base
+
+dataset: https://github.com/ramybaly/Article-Bias-Prediction
+
+
+training parameters:
+- devices: 2xH100
+- batch_size: 100
+- epochs: 5
+- dropout: 0.05
+- max_length: 512
+- learning_rate: 3e-5
+- warmup_steps: 100
+- random_state: 239
+
+
+training methodology:
+- sanitize dataset following specific rule-set, utilize random split as provided in the dataset
+- train on train split and evaluate on validation split in each epoch
+- evaluate test split only on the model that performed best on validation loss
+
+result summary:
+- throughout the five training epochs, model of x epoch achieved the lowest validation loss of x
+- on test split x epoch model achieved f1 score of x and a test loss of x
+
+usage:
+
+```
+model = AutoModelForSequenceClassification.from_pretrained("premsa/political-bias-prediction-allsides-mDeBERTa")
+tokenizer = AutoTokenizer.from_pretrained(premsa/"premsa/political-bias-prediction-allsides-mDeBERTa")
+nlp = pipeline("text-classification", model=model, tokenizer=tokenizer)
+print(nlp("die massen werden von den medien kontrolliert."))
+```