Token Classification
Transformers
Safetensors
Malagasy
xlm-roberta
punctuation-restoration
truecasing
malagasy
asr-postprocessing
Instructions to use Flo976/malagasy-punctuation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Flo976/malagasy-punctuation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Flo976/malagasy-punctuation")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Flo976/malagasy-punctuation") model = AutoModelForTokenClassification.from_pretrained("Flo976/malagasy-punctuation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,589 Bytes
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"architectures": [
"XLMRobertaForTokenClassification"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"dtype": "float32",
"eos_token_id": 2,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "L|O",
"1": "L|,",
"2": "L|.",
"3": "L|?",
"4": "L|!",
"5": "L|:",
"6": "L|;",
"7": "C|O",
"8": "C|,",
"9": "C|.",
"10": "C|?",
"11": "C|!",
"12": "C|:",
"13": "C|;",
"14": "U|O",
"15": "U|,",
"16": "U|.",
"17": "U|?",
"18": "U|!",
"19": "U|:",
"20": "U|;",
"21": "M|O",
"22": "M|,",
"23": "M|.",
"24": "M|?",
"25": "M|!",
"26": "M|:",
"27": "M|;"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"C|!": 11,
"C|,": 8,
"C|.": 9,
"C|:": 12,
"C|;": 13,
"C|?": 10,
"C|O": 7,
"L|!": 4,
"L|,": 1,
"L|.": 2,
"L|:": 5,
"L|;": 6,
"L|?": 3,
"L|O": 0,
"M|!": 25,
"M|,": 22,
"M|.": 23,
"M|:": 26,
"M|;": 27,
"M|?": 24,
"M|O": 21,
"U|!": 18,
"U|,": 15,
"U|.": 16,
"U|:": 19,
"U|;": 20,
"U|?": 17,
"U|O": 14
},
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "xlm-roberta",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"output_past": true,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"transformers_version": "4.57.1",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 250002
}
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