Text Classification
Transformers
Safetensors
bert
sms
spam
phishing
smishing
sms-spam-detection
phishing-detection
fraud-detection
sms-firewall
a2p-messaging
telecom
cybersecurity
text-embeddings-inference
Instructions to use telecomsxchange/OpenTextShield with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use telecomsxchange/OpenTextShield with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="telecomsxchange/OpenTextShield")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("telecomsxchange/OpenTextShield") model = AutoModelForSequenceClassification.from_pretrained("telecomsxchange/OpenTextShield", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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docs: link the company site at www.telecomsxchange.com
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docs: drop numeric language-count claims
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docs: update model id after rename from telecomsxchange/OTS
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docs: SEO/AEO model card — direct answers, at-a-glance facts, carrier usage, citation
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Publish OpenTextShield mBERT v2.7 in standard Transformers format
Verified signatureReplaces the 2024 repository dump with the current production model: safetensors weights, tokenizer, id2label config (ham/spam/phishing) and a model card with honest benchmark results.9bb847f
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