Instructions to use Shaer-AI/ARBERT-base-submeter-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shaer-AI/ARBERT-base-submeter-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Shaer-AI/ARBERT-base-submeter-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Shaer-AI/ARBERT-base-submeter-classifier") model = AutoModelForSequenceClassification.from_pretrained("Shaer-AI/ARBERT-base-submeter-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "BertForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "directionality": "bidi", | |
| "dtype": "float32", | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "baseet complete", | |
| "1": "baseet mukhala", | |
| "2": "hazaj majzuu", | |
| "3": "kamel ahuth", | |
| "4": "kamel complete", | |
| "5": "kamel majzuu", | |
| "6": "khafif complete", | |
| "7": "khafif majzuu", | |
| "8": "madeed majzuu", | |
| "9": "mudari majzuu", | |
| "10": "mujtath majzuu", | |
| "11": "munsarih complete", | |
| "12": "muqtadab majzuu", | |
| "13": "mutadarak complete", | |
| "14": "mutadarak mashture", | |
| "15": "mutaqarib complete", | |
| "16": "rajaz complete", | |
| "17": "rajaz majzuu", | |
| "18": "rajaz mashture", | |
| "19": "ramel complete", | |
| "20": "ramel majzuu", | |
| "21": "saree complete", | |
| "22": "taweel complete", | |
| "23": "wafer complete", | |
| "24": "wafer majzuu" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "baseet complete": 0, | |
| "baseet mukhala": 1, | |
| "hazaj majzuu": 2, | |
| "kamel ahuth": 3, | |
| "kamel complete": 4, | |
| "kamel majzuu": 5, | |
| "khafif complete": 6, | |
| "khafif majzuu": 7, | |
| "madeed majzuu": 8, | |
| "mudari majzuu": 9, | |
| "mujtath majzuu": 10, | |
| "munsarih complete": 11, | |
| "muqtadab majzuu": 12, | |
| "mutadarak complete": 13, | |
| "mutadarak mashture": 14, | |
| "mutaqarib complete": 15, | |
| "rajaz complete": 16, | |
| "rajaz majzuu": 17, | |
| "rajaz mashture": 18, | |
| "ramel complete": 19, | |
| "ramel majzuu": 20, | |
| "saree complete": 21, | |
| "taweel complete": 22, | |
| "wafer complete": 23, | |
| "wafer majzuu": 24 | |
| }, | |
| "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", | |
| "problem_type": "single_label_classification", | |
| "submeter_input_format": "first_hemistich S second_hemistich", | |
| "submeter_missing_second_marker": "E", | |
| "transformers_version": "4.57.6", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 100000 | |
| } | |