Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use tinutmap/categor_ai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tinutmap/categor_ai with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tinutmap/categor_ai")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tinutmap/categor_ai") model = AutoModelForSequenceClassification.from_pretrained("tinutmap/categor_ai", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,335 Bytes
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"_name_or_path": "distilbert-base-uncased",
"activation": "gelu",
"architectures": [
"DistilBertForSequenceClassification"
],
"attention_dropout": 0.1,
"dim": 768,
"dropout": 0.1,
"hidden_dim": 3072,
"id2label": {
"0": "Casegoods",
"1": "Cabinetry",
"2": "Countertop",
"3": "Electrical",
"4": "Elevation",
"5": "Hardware/service parts",
"6": "Keying",
"7": "Legends",
"8": "Panels",
"9": "PicklistScreens",
"10": "Seating",
"11": "Storage",
"12": "Tables",
"13": "Architectual Product",
"14": "Worksurfaces"
},
"initializer_range": 0.02,
"label2id": {
"Architectual Product": 13,
"Cabinetry": 1,
"Casegoods": 0,
"Countertop": 2,
"Electrical": 3,
"Elevation": 4,
"Hardware/service parts": 5,
"Keying": 6,
"Legends": 7,
"Panels": 8,
"PicklistScreens": 9,
"Seating": 10,
"Storage": 11,
"Tables": 12,
"Worksurfaces": 14
},
"max_position_embeddings": 512,
"model_type": "distilbert",
"n_heads": 12,
"n_layers": 6,
"pad_token_id": 0,
"problem_type": "single_label_classification",
"qa_dropout": 0.1,
"seq_classif_dropout": 0.2,
"sinusoidal_pos_embds": false,
"tie_weights_": true,
"torch_dtype": "float32",
"transformers_version": "4.37.2",
"vocab_size": 30522
}
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