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
| { | |
| "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 | |
| } | |