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