original commit

demo.py

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+#high level way
+from transformers import pipeline
+from transformers import AutoConfig
+from transformers import AutoModelForSequenceClassification
+
+#pipeline(task, model, tokenizer, device (0 being gpu))
+#['any-to-any', 'audio-classification', 'automatic-speech-recognition', 'depth-estimation',
+# 'document-question-answering', 'feature-extraction', 'fill-mask', 'image-classification',
+# 'image-feature-extraction', 'image-segmentation', 'image-text-to-text', 'keypoint-matching',
+# 'mask-generation', 'ner'(named entity recognition), 'object-detection', 'sentiment-analysis', 'table-question-answering',
+# 'text-classification', 'text-generation', 'text-to-audio', 'text-to-speech', 'token-classification',
+# 'video-classification', 'zero-shot-audio-classification', 'zero-shot-classification',
+# 'zero-shot-image-classification', 'zero-shot-object-detection']
+
+# Load the model (this downloads weights automatically on first run)
+#classifier = pipeline("text-classification", model="distilbert-base-uncased")
+
+#for mask gen
+predictions = []
+unmasker = pipeline('fill-mask', model='distilbert-base-uncased', device=0)
+results = unmasker("Obama is [MASK] today.", top_k=1)
+predictions.append(results[0])
+
+
+
+unmasker1 = pipeline('fill-mask', model='roberta-base', device=0)
+results1 = unmasker1("Obama is <mask> today.", top_k=1)
+predictions.append(results1[0])
+
+
+unmasker2 = pipeline('fill-mask', model='bert-base-uncased', top_k=1)
+results2 = unmasker2("Obama is [MASK] today.")
+predictions.append(results2[0])
+
+
+best_overall = max(predictions, key=lambda x: x['score'])
+
+print(f"The winning phrase was: {best_overall['sequence']}")
+print(f"With a confidence score of: {best_overall['score']:.4f}")

distilbert.py

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+from transformers import pipeline
+import numpy
+# distilbert tasks
+# fill-mask
+# feature extraction
+# have to use more specific models/other models for other tasks
+
+#for mask gen
+unmasker = pipeline('fill-mask', model='distilbert-base-uncased', device=0)
+results = unmasker("I [MASK] today.", top_k=10)
+for prediction in results:
+    print(f"Score: {prediction['score']:.4f} | Prediction: {prediction['sequence']}")
+
+#for feature extraction
+extractor = pipeline('feature-extraction', model='distilbert-base-uncased', device=0)
+results = extractor("Obama")
+
+#idk what this really does
+features = numpy.array(results)
+print(f"Shape: {features.shape}")

google.py

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+from transformers import pipeline
+# google bert tasks
+# fill-mask
+# feature extraction
+# have to use more specific models/other models for other tasks
+# for mask gen
+unmasker = pipeline('fill-mask', model='bert-base-uncased')
+results = unmasker("Obama is [MASK].")
+for prediction in results:
+    print(f"Score: {prediction['score']:.4f} | Prediction: {prediction['sequence']}")

roberta.py

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+from transformers import pipeline
+# roberta tasks
+# fill-mask
+# feature extraction
+# have to use more specific models/other models for other tasks
+# for mask gen
+unmasker = pipeline('fill-mask', model='roberta-base', device=0)
+results = unmasker("Obama is <mask>.", top_k=10)
+for prediction in results:
+    print(f"Score: {prediction['score']:.4f} | Prediction: {prediction['sequence']}")