Update README.md

README.md

@@ -5,7 +5,8 @@ language:
 tags:
 - pytorch
 - ner
-- qa
+- text generation
+- seq2seq
 
 inference: false
 
@@ -36,10 +37,23 @@ model = Model(
     model_path_or_name="olgaduchovny/t5-base-ner-mit-movie",
     tokenizer_path_or_name="olgaduchovny/t5-base-ner-mit-movie"
 )
-options = ["LOC", "PER", "ORG", "MISC"]
+options = [
+        "ACTOR",
+        "AWARD",
+        "CHARACTER",
+        "DIRECTOR",
+        "GENRE",
+        "OPINION",
+        "ORIGIN",
+        "PLOT",
+        "QUOTE",
+        "RELATIONSHIP",
+        "SOUNDTRACK",
+        "YEAR"
+    ]
 instruction = "please extract entities and their types from the input sentence, " \
               "all entity types are in options"
-text = "The protest , which attracted several thousand supporters , coincided with the 18th anniversary of Spain 's constitution ."
+text = "are there any good romantic comedies out right now"
 generation_kwargs = {
     "num_beams": 2,
     "max_length": 128
@@ -50,14 +64,5 @@ pred_spans = model.predict(
     instruction=instruction,
     options=options
 )
->>> [(99, 104, 'LOC')]
-```
-
-## Prediction Sample
-```
-Sentence: The protest , which attracted several thousand supporters , coincided with the 18th anniversary of Spain 's constitution .
-Instruction: please extract entities and their types from the input sentence, all entity types are in options
-Options: ORG, PER, LOC
-Prediction (raw text): Spain is a LOC.
-Prediction (span): [(99, 104, 'LOC')]
-```
+>>> [(19, 36, 'GENRE'), (41, 50, 'YEAR')]
+```