Instructions to use Delphia/twitter-spam-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Delphia/twitter-spam-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Delphia/twitter-spam-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Delphia/twitter-spam-classifier") model = AutoModelForSequenceClassification.from_pretrained("Delphia/twitter-spam-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Delphia/twitter-spam-classifier: direct link, hf CLI and curl.
- Browser
- Download file 552 Bytes
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https://huggingface.co/Delphia/twitter-spam-classifier/resolve/main/README.md
- Command line
-
hf download hf://Delphia/twitter-spam-classifier/README.md
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curl -L -o README.md https://huggingface.co/Delphia/twitter-spam-classifier/resolve/main/README.md
552 Bytes
metadata
tags:
- autotrain
- text-classification
widget:
- text: I love AutoTrain
datasets:
- autotrain-57208-4mv8z/autotrain-data
Model Trained Using AutoTrain
- Problem type: Text Classification
Model trained on "Tesla" related tweets from X/Twitter to filter out spam tweets based on trolling, profanity, extreme political views, etc.
0 - Valid
1 - Spam
Validation Metrics
loss: 0.4916948974132538
f1: 0.8059701492537313
precision: 0.782608695652174
recall: 0.8307692307692308
auc: 0.8416783216783217
accuracy: 0.7833333333333333