Instructions to use EmnaBou/TD-tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EmnaBou/TD-tokenizer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="EmnaBou/TD-tokenizer")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("EmnaBou/TD-tokenizer") model = AutoModelForTokenClassification.from_pretrained("EmnaBou/TD-tokenizer") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_num_labels": 6, | |
| "architectures": [ | |
| "BertForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "B_RM", | |
| "1": "B_RP", | |
| "2": "IP", | |
| "3": "I_RM", | |
| "4": "I_RP", | |
| "5": "O" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "B_RM": 0, | |
| "B_RP": 1, | |
| "IP": 2, | |
| "I_RM": 3, | |
| "I_RP": 4, | |
| "O": 5 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "output_past": true, | |
| "pad_token_id": 0, | |
| "type_vocab_size": 2, | |
| "vocab_size": 32359 | |
| } | |