Instructions to use beloiual/model_3_edges with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use beloiual/model_3_edges with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="beloiual/model_3_edges")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("beloiual/model_3_edges") model = AutoModelForTokenClassification.from_pretrained("beloiual/model_3_edges", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "distilbert/distilbert-base-uncased", | |
| "activation": "gelu", | |
| "architectures": [ | |
| "DistilBertForTokenClassification" | |
| ], | |
| "attention_dropout": 0.1, | |
| "dim": 768, | |
| "dropout": 0.1, | |
| "hidden_dim": 3072, | |
| "id2label": { | |
| "0": "[0, 0, 0]", | |
| "1": "[0, 0, 1]", | |
| "2": "[0, 1, 0]", | |
| "3": "[0, 1, 1]", | |
| "4": "[1, 0, 0]", | |
| "5": "[1, 0, 1]", | |
| "6": "[1, 1, 0]", | |
| "7": "[1, 1, 1]" | |
| }, | |
| "initializer_range": 0.02, | |
| "label2id": { | |
| "[0, 0, 0]": 0, | |
| "[0, 0, 1]": 1, | |
| "[0, 1, 0]": 2, | |
| "[0, 1, 1]": 3, | |
| "[1, 0, 0]": 4, | |
| "[1, 0, 1]": 5, | |
| "[1, 1, 0]": 6, | |
| "[1, 1, 1]": 7 | |
| }, | |
| "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, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.40.0", | |
| "vocab_size": 30522 | |
| } | |