Instructions to use ArabicNewsAnalyzer/MARBERTv2-Single-Arabic-Dialect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ArabicNewsAnalyzer/MARBERTv2-Single-Arabic-Dialect with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ArabicNewsAnalyzer/MARBERTv2-Single-Arabic-Dialect")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ArabicNewsAnalyzer/MARBERTv2-Single-Arabic-Dialect") model = AutoModelForSequenceClassification.from_pretrained("ArabicNewsAnalyzer/MARBERTv2-Single-Arabic-Dialect", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_cross_attention": false, | |
| "architectures": [ | |
| "BertForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": null, | |
| "classifier_dropout": null, | |
| "directionality": "bidi", | |
| "dtype": "float16", | |
| "eos_token_id": null, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "Algeria", | |
| "1": "Bahrain", | |
| "2": "Egypt", | |
| "3": "Iraq", | |
| "4": "Jordan", | |
| "5": "Kuwait", | |
| "6": "Lebanon", | |
| "7": "Libya", | |
| "8": "Morocco", | |
| "9": "Oman", | |
| "10": "Palestine", | |
| "11": "Qatar", | |
| "12": "Saudi_Arabia", | |
| "13": "Sudan", | |
| "14": "Syria", | |
| "15": "Tunisia", | |
| "16": "UAE", | |
| "17": "Yemen" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "is_decoder": false, | |
| "label2id": { | |
| "Algeria": 0, | |
| "Bahrain": 1, | |
| "Egypt": 2, | |
| "Iraq": 3, | |
| "Jordan": 4, | |
| "Kuwait": 5, | |
| "Lebanon": 6, | |
| "Libya": 7, | |
| "Morocco": 8, | |
| "Oman": 9, | |
| "Palestine": 10, | |
| "Qatar": 11, | |
| "Saudi_Arabia": 12, | |
| "Sudan": 13, | |
| "Syria": 14, | |
| "Tunisia": 15, | |
| "UAE": 16, | |
| "Yemen": 17 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "pooler_fc_size": 768, | |
| "pooler_num_attention_heads": 12, | |
| "pooler_num_fc_layers": 3, | |
| "pooler_size_per_head": 128, | |
| "pooler_type": "first_token_transform", | |
| "position_embedding_type": "absolute", | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.13.1", | |
| "type_vocab_size": 2, | |
| "use_cache": false, | |
| "vocab_size": 100000 | |
| } | |