Text Classification
Transformers
ONNX
Safetensors
Chinese
bert
chinese
intent-classification
text-embeddings-inference
Instructions to use pawizard/traffic-classify with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pawizard/traffic-classify with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pawizard/traffic-classify")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pawizard/traffic-classify") model = AutoModelForSequenceClassification.from_pretrained("pawizard/traffic-classify", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,067 Bytes
aa7739c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | {
"architectures": [
"BertForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"directionality": "bidi",
"dtype": "float32",
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "\u975e\u7814\u53d1\u76f8\u5173",
"1": "\u7814\u53d1\u76f8\u5173",
"2": "\u4e2d\u6027"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"\u4e2d\u6027": 2,
"\u7814\u53d1\u76f8\u5173": 1,
"\u975e\u7814\u53d1\u76f8\u5173": 0
},
"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",
"transformers_version": "4.57.6",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 21128
}
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