Update README.md

README.md

@@ -17,6 +17,49 @@ SpEL model finetuned on English Wikipedia as well as the training portion of CoN
 It is introduced in the paper [SPEL: Structured Prediction for Entity Linking (EMNLP 2023)](https://arxiv.org/abs/2310.14684). 
 The code and data are available in [this repository](https://github.com/shavarani/SpEL).
 
+### Usage
+The following snippet demonstrates a quick way that SpEL can be used to generate subword-level, word-level, and phrase-level annotations for a sentence.
+
+```python
+# download SpEL from https://github.com/shavarani/SpEL
+from transformers import AutoTokenizer
+from spel.model import SpELAnnotator
+from spel.configuration import device
+from spel.utils import get_subword_to_word_mapping
+from spel.span_annotation import WordAnnotation, PhraseAnnotation
+finetuned_after_step = 4
+sentence = "Grace Kelly by Mika reached the top of the UK Singles Chart in 2007."
+tokenizer = AutoTokenizer.from_pretrained("roberta-base")
+# ############################################# LOAD SpEL #############################################################
+spel = SpELAnnotator()
+spel.init_model_from_scratch(device=device)
+if finetuned_after_step == 3:
+    spel.shrink_classification_head_to_aida(device)
+spel.load_checkpoint(None, device=device, load_from_torch_hub=True, finetuned_after_step=finetuned_after_step)
+# ############################################# RUN SpEL ##############################################################
+inputs = tokenizer(sentence, return_tensors="pt")
+token_offsets = list(zip(inputs.encodings[0].tokens,inputs.encodings[0].offsets))
+subword_annotations = spel.annotate_subword_ids(inputs.input_ids, k_for_top_k_to_keep=10, token_offsets=token_offsets)
+# #################################### CREATE WORD-LEVEL ANNOTATIONS ##################################################
+tokens_offsets = token_offsets[1:-1]
+subword_annotations = subword_annotations[1:]
+word_annotations = [WordAnnotation(subword_annotations[m[0]:m[1]], tokens_offsets[m[0]:m[1]])
+                    for m in get_subword_to_word_mapping(inputs.tokens(), sentence)]
+# ################################## CREATE PHRASE-LEVEL ANNOTATIONS ##################################################
+phrase_annotations = []
+for w in word_annotations:
+    if not w.annotations:
+        continue
+    if phrase_annotations and phrase_annotations[-1].resolved_annotation == w.resolved_annotation:
+        phrase_annotations[-1].add(w)
+    else:
+        phrase_annotations.append(PhraseAnnotation(w))
+```
+
+
+
+
+
 ## Evaluation Results
 Entity Linking evaluation results of *SpEL* compared to that of the literature over AIDA test sets: