Text Classification
Transformers
Safetensors
distilbert
0.0.2
Generated from Trainer
text-embeddings-inference
Instructions to use tinutmap/ai-categories-text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tinutmap/ai-categories-text with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tinutmap/ai-categories-text")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tinutmap/ai-categories-text") model = AutoModelForSequenceClassification.from_pretrained("tinutmap/ai-categories-text", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,494 Bytes
050e3e9 | 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 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | {
"activation": "gelu",
"architectures": [
"DistilBertForSequenceClassification"
],
"attention_dropout": 0.1,
"dim": 768,
"dropout": 0.1,
"hidden_dim": 3072,
"id2label": {
"0": "Accessories",
"1": "Architectural",
"2": "Casegoods",
"3": "Connectors",
"4": "Desking",
"5": "Electrical",
"6": "Flooring",
"7": "Lighting",
"8": "Panels",
"9": "Screens",
"10": "Seating",
"11": "Space Division",
"12": "Storage",
"13": "Supports",
"14": "Tables",
"15": "Tackboards-Markerboards",
"16": "Technology",
"17": "Worksurfaces",
"18": "Unknown",
"19": "Workspaces"
},
"initializer_range": 0.02,
"label2id": {
"Accessories": 0,
"Architectural": 1,
"Casegoods": 2,
"Connectors": 3,
"Desking": 4,
"Electrical": 5,
"Flooring": 6,
"Lighting": 7,
"Panels": 8,
"Screens": 9,
"Seating": 10,
"Space Division": 11,
"Storage": 12,
"Supports": 13,
"Tables": 14,
"Tackboards-Markerboards": 15,
"Technology": 16,
"Unknown": 18,
"Workspaces": 19,
"Worksurfaces": 17
},
"max_position_embeddings": 512,
"model_type": "distilbert",
"n_heads": 12,
"n_layers": 6,
"pad_token_id": 0,
"problem_type": "multi_label_classification",
"qa_dropout": 0.1,
"seq_classif_dropout": 0.2,
"sinusoidal_pos_embds": false,
"tie_weights_": true,
"torch_dtype": "float32",
"transformers_version": "4.52.2",
"vocab_size": 30522
}
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