Update app.py

app.py

@@ -18,9 +18,11 @@ Two main tasks are available:
 
 - Sentiment Analysis (SA), with two English-only models (one for classification, one for generation) and a large multilingual model for classification.
 
+- Relation Extraction (RE), with an English-only model that identifies relevant characters and existing relations between them following the Activity feature of the the Hall and Van de Castle framework.
+
 - Name Entity Recognition (NER), with an English-only model that generates the identified characters. 
 
-All models have been tuned on the Hall and Vand de Castle framework. More details are on the page for each model. For more on the training framework, see the [Bertolini et al., 2023](https://arxiv.org/pdf/2302.14828.pdf) preprint.
+All models have been tuned on the Hall and Van de Castle framework. More details are on the page for each model. For more on the training framework, see the [Bertolini et al., 2023](https://arxiv.org/pdf/2302.14828.pdf) preprint.
 
 Use the current interface to check if a language is included in the multilingual SA model, using language acronyms (e.g. it for Italian). the tabs above will direct you to each model to query.
 
@@ -32,7 +34,7 @@ This model is an XLM-R tuned model, pre-trained with 94 languages available, and
 """
 
 description_S = """
-A BERT-base-cased model pre-trained on Eglish-only text and tuned on annotated DreamBank English data..
+A BERT-base-cased model pre-trained on Eglish-only text and tuned on annotated DreamBank English data.
 """
 
 description_G = """
@@ -56,6 +58,9 @@ examples_g = [
 
 ]
 
+examples_re = [
+        ["I was talking on the telephone to the father of an old friend of mine (boy, 21 years old). We were discussing the party the Saturday night before to which I had invited his son as a guest. I asked him if his son had a good time at the party. He told me not to tell his son that he had told me, but that he had had a good time, except he was a little surprised that I had acted the way I did."]
+]
 
 interface_words = gr.Interface(
             fn=check_lang,
@@ -70,21 +75,28 @@ interface_model_L = gr.Interface.load(
             name="huggingface/DReAMy-lib/xlm-roberta-large-DreamBank-emotion-presence",
             description=description_L,
             examples=examples,
-            title="DSA Large Multilingual",
+            title="SA Large Multilingual",
 )
 
 interface_model_S = gr.Interface.load(
             name="huggingface/DReAMy-lib/bert-base-cased-DreamBank-emotion-presence",
             description=description_S,
             examples=examples[0],
-            title="DSA Base English-Only",
+            title="SA Base English-Only",
 )
 
 interface_model_G = gr.Interface.load(
             name="huggingface/DReAMy-lib/t5-base-DreamBank-Generation-Emot-Char",
             description=description_G,
             examples=examples_g,
-            title="DSA Generation",
+            title="SA Generation",
+)
+
+interface_model_RE = gr.Interface.load(
+            name="huggingface/DReAMy-lib/t5-base-DreamBank-Generation-Act-Char",
+            description=description_G,
+            examples=examples_re,
+            title="RE Generation",
 )
 
 interface_model_NER = gr.Interface.load(
@@ -95,6 +107,6 @@ interface_model_NER = gr.Interface.load(
 )
 
 gr.TabbedInterface(
-    [interface_words, interface_model_L, interface_model_S, interface_model_G, interface_model_NER], 
-    ["Intro", "SA Large Multilingual", "SA Base En", "SA En Generation", "NER Generation"]
+    [interface_words, interface_model_L, interface_model_S, interface_model_G, interface_model_RE, interface_model_NER], 
+    ["Intro", "SA Large Multilingual", "SA Base En", "SA En Generation", "RE Generation", "NER Generation"]
 ).launch()