add model weights

.gitattributes

@@ -29,3 +29,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.json filter=lfs diff=lfs merge=lfs -text

app.py

@@ -3,14 +3,30 @@ from transformers import pipeline
 
 import gradio as gr
 
-ner_pipeline = pipeline("ner")
+
+model_name="xlm-roberta-base"
+# model_name="roberta-large"  
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+
+
+label_list= ['literal',"metaphoric"]
+label_dict_relations={ i : l for i, l in enumerate(label_list) }
+PATH = "./saved-models/my_model"
+model_metaphor_detection = AutoModelForTokenClassification.from_pretrained(PATH, id2label=label_dict_relations)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+pipeline_metaphors=pipeline("ner", model=model_metaphor_detection, tokenizer=tokenizer, aggregation_strategy="simple")
 
 examples = [
-    "Does Chicago have any stores and does Joe live here?",
+    "It would change the trajectory of your legal career.",
+    "Washington and the media just explodes on you, you just don’t know where you are at the moment", 
+    "Those statements are deeply concerning.", 
 ]
 
 def ner(text):
-    output = ner_pipeline(text)
+    output = pipeline_metaphors(text)
+    # change name 
+    for x in output:
+        x['entity'] = x['entity_group']
     return {"text": text, "entities": output}    
 
 demo = gr.Interface(ner,
@@ -18,4 +34,4 @@ demo = gr.Interface(ner,
              gr.HighlightedText(),
              examples=examples)
 
-demo.launch()
+demo.launch(share=True)

saved-models/my_model/config.json

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saved-models/my_model/pytorch_model.bin

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saved-models/my_model/special_tokens_map.json

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saved-models/my_model/tokenizer.json

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saved-models/my_model/tokenizer_config.json

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saved-models/my_model/training_args.bin

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