Text Classification
Transformers
Safetensors
German
English
bert
klarki
eu-ai-act
compliance
german
Eval Results (legacy)
Instructions to use s4nkar/klarki-bert-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use s4nkar/klarki-bert-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="s4nkar/klarki-bert-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("s4nkar/klarki-bert-classifier") model = AutoModelForSequenceClassification.from_pretrained("s4nkar/klarki-bert-classifier") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "BertForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "risk_management", | |
| "1": "data_governance", | |
| "2": "technical_documentation", | |
| "3": "record_keeping", | |
| "4": "transparency", | |
| "5": "human_oversight", | |
| "6": "security", | |
| "7": "unrelated" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "data_governance": 1, | |
| "human_oversight": 5, | |
| "record_keeping": 3, | |
| "risk_management": 0, | |
| "security": 6, | |
| "technical_documentation": 2, | |
| "transparency": 4, | |
| "unrelated": 7 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "transformers_version": "4.57.6", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 31102 | |
| } | |