Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use LovenOO/distilBERT_with_preprocessing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LovenOO/distilBERT_with_preprocessing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LovenOO/distilBERT_with_preprocessing")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LovenOO/distilBERT_with_preprocessing") model = AutoModelForSequenceClassification.from_pretrained("LovenOO/distilBERT_with_preprocessing", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "distilbert-base-uncased", | |
| "activation": "gelu", | |
| "architectures": [ | |
| "DistilBertForSequenceClassification" | |
| ], | |
| "attention_dropout": 0.1, | |
| "dim": 768, | |
| "dropout": 0.1, | |
| "hidden_dim": 3072, | |
| "id2label": { | |
| "0": "Population and Society", | |
| "1": "Children Education and Skills", | |
| "2": "Gaps", | |
| "3": "Covid", | |
| "4": "Crime and Security", | |
| "5": "Climate change", | |
| "6": "Transport", | |
| "7": "Economy", | |
| "8": "Poverty", | |
| "9": "Children Education and Skills (Post-16)", | |
| "10": "Environment", | |
| "11": "census_accounts", | |
| "12": "Health and Social Care", | |
| "13": "Housing Planning and Local Services", | |
| "14": "Statistical Literacy", | |
| "15": "accounts", | |
| "16": "Children", | |
| "17": "NI Health", | |
| "18": "Ukraine", | |
| "19": "Labour Market and Welfare" | |
| }, | |
| "initializer_range": 0.02, | |
| "label2id": { | |
| "Children": 16, | |
| "Children Education and Skills": 1, | |
| "Children Education and Skills (Post-16)": 9, | |
| "Climate change": 5, | |
| "Covid": 3, | |
| "Crime and Security": 4, | |
| "Economy": 7, | |
| "Environment": 10, | |
| "Gaps": 2, | |
| "Health and Social Care": 12, | |
| "Housing Planning and Local Services": 13, | |
| "Labour Market and Welfare": 19, | |
| "NI Health": 17, | |
| "Population and Society": 0, | |
| "Poverty": 8, | |
| "Statistical Literacy": 14, | |
| "Transport": 6, | |
| "Ukraine": 18, | |
| "accounts": 15, | |
| "census_accounts": 11 | |
| }, | |
| "max_position_embeddings": 512, | |
| "model_type": "distilbert", | |
| "n_heads": 12, | |
| "n_layers": 6, | |
| "pad_token_id": 0, | |
| "qa_dropout": 0.1, | |
| "seq_classif_dropout": 0.2, | |
| "sinusoidal_pos_embds": false, | |
| "tie_weights_": true, | |
| "transformers_version": "4.24.0", | |
| "vocab_size": 30522 | |
| } | |