Instructions to use ldenoue/punctuator-tiny-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ldenoue/punctuator-tiny-multilingual with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ldenoue/punctuator-tiny-multilingual")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ldenoue/punctuator-tiny-multilingual") model = AutoModelForTokenClassification.from_pretrained("ldenoue/punctuator-tiny-multilingual", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,367 Bytes
71e731d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | {
"architectures": [
"BertForTokenClassification"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"dtype": "float32",
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 384,
"id2label": {
"0": "LOWER|NONE",
"1": "LOWER|COMMA",
"2": "LOWER|PERIOD",
"3": "LOWER|QUESTION",
"4": "LOWER|EXCLAMATION",
"5": "CAP|NONE",
"6": "CAP|COMMA",
"7": "CAP|PERIOD",
"8": "CAP|QUESTION",
"9": "CAP|EXCLAMATION",
"10": "UPPER|NONE",
"11": "UPPER|COMMA",
"12": "UPPER|PERIOD",
"13": "UPPER|QUESTION",
"14": "UPPER|EXCLAMATION"
},
"initializer_range": 0.02,
"intermediate_size": 1536,
"label2id": {
"CAP|COMMA": 6,
"CAP|EXCLAMATION": 9,
"CAP|NONE": 5,
"CAP|PERIOD": 7,
"CAP|QUESTION": 8,
"LOWER|COMMA": 1,
"LOWER|EXCLAMATION": 4,
"LOWER|NONE": 0,
"LOWER|PERIOD": 2,
"LOWER|QUESTION": 3,
"UPPER|COMMA": 11,
"UPPER|EXCLAMATION": 14,
"UPPER|NONE": 10,
"UPPER|PERIOD": 12,
"UPPER|QUESTION": 13
},
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 6,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.57.6",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 32000
}
|