First commit

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard 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

@@ -0,0 +1,26 @@
+import gradio as gr
+
+from datasets import load_dataset
+from transformers import AutoTokenizer, DataCollatorWithPadding
+from transformers import TrainingArguments
+from transformers import AutoModelForSequenceClassification
+from transformers import Trainer
+from datasets import load_metric
+import numpy as np
+from transformers import pipeline
+
+
+model_loader = AutoModelForSequenceClassification.from_pretrained("content/models")
+tokenizer_loader = AutoTokenizer.from_pretrained("content/models")
+model_loader.eval()
+print("loaded")
+
+
+classifier = pipeline("sentiment-analysis", model=model_loader, tokenizer=tokenizer_loader, device=0)
+
+def greet(twitter):
+    pred = classifier(twitter)[0]
+    return "twitter is %s with score=%.4f" % (pred['label'], pred['score'])
+
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()

content/models/config.json

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content/models/pytorch_model.bin

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content/models/special_tokens_map.json

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content/models/tokenizer.json

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content/models/tokenizer_config.json

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content/models/training_args.bin

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content/models/unigram.json

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