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
Download config.json from telecomsxchange/OpenTextShield: direct link, hf CLI and curl.
- Browser
- Download file 937 Bytes
-
https://huggingface.co/telecomsxchange/OpenTextShield/resolve/main/config.json
- Command line
-
hf download hf://telecomsxchange/OpenTextShield/config.json
-
curl -L -o config.json https://huggingface.co/telecomsxchange/OpenTextShield/resolve/main/config.json
937 Bytes
| { | |
| "architectures": [ | |
| "BertForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "directionality": "bidi", | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "ham", | |
| "1": "spam", | |
| "2": "phishing" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "ham": 0, | |
| "phishing": 2, | |
| "spam": 1 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "pooler_fc_size": 768, | |
| "pooler_num_attention_heads": 12, | |
| "pooler_num_fc_layers": 3, | |
| "pooler_size_per_head": 128, | |
| "pooler_type": "first_token_transform", | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.53.0", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 119547 | |
| } | |