Update README.md

README.md

@@ -19,11 +19,12 @@ The model is based on the [North-T5-NCC Large](https://huggingface.co/north/t5_l
 - **Model type:** Language model restricted to classification
 - **Language(s) (NLP):** Danish and Norwegian
 - **License:** [More Information Needed]
-- **Finetuned from model:** [More information needed] 
+- **Finetuned from model:** [North-T5-NCC Large](https://huggingface.co/north/t5_large_NCC)
 
 
 # Direct Use
-The model can be used directly to classify Danish and Norwegian social media posts (or similar pieces of text).
+This model can be used for classifying Danish and Norwegian social media posts or similar text.
+
 
 
 # Bias, Risks, and Limitations
@@ -33,7 +34,8 @@ The model can be used directly to classify Danish and Norwegian social media pos
 # Training Data
 A collection of ~70k Norwegian and ~67k Danish social media posts have been manually annotated as 'attack' or 'not attack' by six individual coders. 5% of the posts have been annotated by more then one annotator, with the annotators in agreement for 83% of annotations.
 
-*Hvad er data-split metoden? Hvad er training-validation-test split?*
+[More information needed on the data split method and the training-validation-test split.]
+
 
 # Evaluation
 <!-- This section describes the evaluation protocols and provides the results. -->
@@ -94,7 +96,7 @@ model = AutoModelForSeq2SeqLM.from_pretrained("ogtal/A-og-ttack2")
 
 # Give sample text. The example is from a social media comment.
 sample_text = "Velbekomme dit klamme usle løgnersvin!"
-input_ids = tokenizer("Velbekomme", return_tensors="pt").input_ids
+input_ids = tokenizer(sample_text, return_tensors="pt").input_ids
 
 # Forward pass and print the output
 outputs = model.generate(input_ids)