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
File size: 710 Bytes
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"_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
}
|