Update README.md

README.md

@@ -1,3 +1,30 @@
----
-license: mit
----
+---
+license: mit
+---
+This is a NER model meant to be used to detect/extract citations from American legal documents.
+
+Ignore the widget on the model card page; see below for usage. 
+
+## How to Use the Model
+
+This model outputs token-level predictions, which should be processed as follows to obtain meaningful labels for each token:
+
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("ss108/legal-citation-bert")
+model = AutoModelForTokenClassification.from_pretrained("ss108/legal-citation-bert")
+
+text = "Your example text here"
+inputs = tokenizer(text, return_tensors="pt", padding=True)
+outputs = model(**inputs)
+
+logits = outputs.logits
+predictions = torch.argmax(logits, dim=-1)
+
+tokens = tokenizer.convert_ids_to_tokens(inputs['input_ids'][0])
+predicted_labels = [model.config.id2label[p.item()] for p in predictions[0]]
+
+for token, label in zip(tokens, predicted_labels):
+    print(f"{token}: {label}")