Instructions to use SterlingWork/sdg-classifier-multilabel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SterlingWork/sdg-classifier-multilabel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SterlingWork/sdg-classifier-multilabel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SterlingWork/sdg-classifier-multilabel") model = AutoModelForSequenceClassification.from_pretrained("SterlingWork/sdg-classifier-multilabel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "ModernBertForSequenceClassification" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 50281, | |
| "classifier_activation": "gelu", | |
| "classifier_bias": false, | |
| "classifier_dropout": 0.0, | |
| "classifier_pooling": "mean", | |
| "cls_token_id": 50281, | |
| "decoder_bias": true, | |
| "deterministic_flash_attn": false, | |
| "dtype": "float32", | |
| "embedding_dropout": 0.0, | |
| "eos_token_id": 50282, | |
| "global_attn_every_n_layers": 3, | |
| "global_rope_theta": 160000.0, | |
| "gradient_checkpointing": false, | |
| "hidden_activation": "gelu", | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "SDG 1", | |
| "1": "SDG 2", | |
| "2": "SDG 3", | |
| "3": "SDG 4", | |
| "4": "SDG 5", | |
| "5": "SDG 6", | |
| "6": "SDG 7", | |
| "7": "SDG 8", | |
| "8": "SDG 9", | |
| "9": "SDG 10", | |
| "10": "SDG 11", | |
| "11": "SDG 12", | |
| "12": "SDG 13", | |
| "13": "SDG 14", | |
| "14": "SDG 15", | |
| "15": "SDG 16" | |
| }, | |
| "initializer_cutoff_factor": 2.0, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1152, | |
| "label2id": { | |
| "SDG 1": 0, | |
| "SDG 10": 9, | |
| "SDG 11": 10, | |
| "SDG 12": 11, | |
| "SDG 13": 12, | |
| "SDG 14": 13, | |
| "SDG 15": 14, | |
| "SDG 16": 15, | |
| "SDG 2": 1, | |
| "SDG 3": 2, | |
| "SDG 4": 3, | |
| "SDG 5": 4, | |
| "SDG 6": 5, | |
| "SDG 7": 6, | |
| "SDG 8": 7, | |
| "SDG 9": 8 | |
| }, | |
| "layer_norm_eps": 1e-05, | |
| "local_attention": 128, | |
| "local_rope_theta": 10000.0, | |
| "max_position_embeddings": 8192, | |
| "mlp_bias": false, | |
| "mlp_dropout": 0.0, | |
| "model_type": "modernbert", | |
| "norm_bias": false, | |
| "norm_eps": 1e-05, | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 22, | |
| "pad_token_id": 50283, | |
| "position_embedding_type": "absolute", | |
| "problem_type": "multi_label_classification", | |
| "repad_logits_with_grad": false, | |
| "sep_token_id": 50282, | |
| "sparse_pred_ignore_index": -100, | |
| "sparse_prediction": false, | |
| "transformers_version": "4.57.3", | |
| "vocab_size": 50368 | |
| } | |