Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use gerbejon/digilog-eform-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gerbejon/digilog-eform-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gerbejon/digilog-eform-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gerbejon/digilog-eform-classifier") model = AutoModelForSequenceClassification.from_pretrained("gerbejon/digilog-eform-classifier") - Notebooks
- Google Colab
- Kaggle
File size: 1,142 Bytes
0504d47 | 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 | {
"_name_or_path": "distilbert-base-multilingual-cased",
"activation": "gelu",
"architectures": [
"DistilBertForSequenceClassification"
],
"attention_dropout": 0.1,
"dim": 768,
"dropout": 0.1,
"hidden_dim": 3072,
"id2label": {
"0": "Diverses",
"1": "Bau",
"2": "Steuern Finanzen",
"3": "SBB Verkehr",
"4": "Living Moving",
"5": "Ausweise",
"6": "Kontakt",
"7": "Newsletter",
"8": "Tiere",
"9": "Vereine Busines"
},
"initializer_range": 0.02,
"label2id": {
"Ausweise": "5",
"Bau": "1",
"Diverses": "0",
"Kontakt": "6",
"Living Moving": "4",
"Newsletter": "7",
"SBB Verkehr": "3",
"Steuern Finanzen": "2",
"Tiere": "8",
"Vereine Busines": "9"
},
"max_position_embeddings": 512,
"model_type": "distilbert",
"n_heads": 12,
"n_layers": 6,
"output_past": true,
"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": "4.49.0",
"vocab_size": 119547
}
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