ucirvine/sms_spam
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How to use mariagrandury/distilbert-base-uncased-finetuned-sms-spam-detection with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="mariagrandury/distilbert-base-uncased-finetuned-sms-spam-detection") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("mariagrandury/distilbert-base-uncased-finetuned-sms-spam-detection")
model = AutoModelForSequenceClassification.from_pretrained("mariagrandury/distilbert-base-uncased-finetuned-sms-spam-detection", device_map="auto")This model is a fine-tuned version of distilbert-base-uncased on the sms_spam dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0375 | 1.0 | 262 | 0.0549 | 0.9892 |
| 0.0205 | 2.0 | 524 | 0.0426 | 0.9921 |