Token Classification
Transformers
Safetensors
Moroccan Arabic
Arabic
bert
part-of-speech
pos-tagging
moroccan-darija
darija
low-resource-languages
Eval Results (legacy)
Instructions to use TypicaAI/DarijaPOSTagger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TypicaAI/DarijaPOSTagger with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="TypicaAI/DarijaPOSTagger")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("TypicaAI/DarijaPOSTagger") model = AutoModelForTokenClassification.from_pretrained("TypicaAI/DarijaPOSTagger") - Notebooks
- Google Colab
- Kaggle
File size: 1,495 Bytes
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"add_cross_attention": false,
"architectures": [
"BertForTokenClassification"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": null,
"classifier_dropout": null,
"cls_token": "[CLS]",
"do_lower_case": true,
"dtype": "float32",
"eos_token_id": null,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "O",
"1": "ADJ",
"2": "ADV",
"3": "CASE",
"4": "CONJ",
"5": "DET",
"6": "FUT_PART",
"7": "NEG_PART",
"8": "NOUN",
"9": "NSUFF",
"10": "PART",
"11": "PREP",
"12": "PROG_PART",
"13": "PRON",
"14": "V"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"is_decoder": false,
"label2id": {
"ADJ": 1,
"ADV": 2,
"CASE": 3,
"CONJ": 4,
"DET": 5,
"FUT_PART": 6,
"NEG_PART": 7,
"NOUN": 8,
"NSUFF": 9,
"O": 0,
"PART": 10,
"PREP": 11,
"PROG_PART": 12,
"PRON": 13,
"V": 14
},
"layer_norm_eps": 1e-12,
"mask_token": "[MASK]",
"max_len": 128,
"max_position_embeddings": 512,
"model_max_length": 128,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token": "[PAD]",
"pad_token_id": 0,
"position_embedding_type": "absolute",
"sep_token": "[SEP]",
"tie_word_embeddings": true,
"transformers_version": "5.12.1",
"type_vocab_size": 2,
"unk_token": "[UNK]",
"use_cache": true,
"vocab_size": 80000
}
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