Instructions to use QomSSLab/SubjectClassifier-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QomSSLab/SubjectClassifier-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="QomSSLab/SubjectClassifier-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("QomSSLab/SubjectClassifier-v1") model = AutoModelForSequenceClassification.from_pretrained("QomSSLab/SubjectClassifier-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,664 Bytes
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"add_cross_attention": false,
"architectures": [
"XLMRobertaForSequenceClassification"
],
"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": 1024,
"id2label": {
"0": "\u0627\u062e\u0644\u0627\u0642",
"1": "\u062a\u0627\u0631\u06cc\u062e",
"2": "\u062d\u062f\u06cc\u062b",
"3": "\u0633\u06cc\u0627\u0633\u06cc",
"4": "\u0641\u0642\u0647 \u0648 \u0627\u062d\u06a9\u0627\u0645",
"5": "\u0642\u0631\u0622\u0646",
"6": "\u0645\u0634\u0627\u0648\u0631\u0647",
"7": "\u06a9\u0644\u0627\u0645 \u0648 \u0627\u0639\u062a\u0642\u0627\u062f\u0627\u062a"
},
"initializer_range": 0.02,
"intermediate_size": 4096,
"is_decoder": false,
"label2id": {
"\u0627\u062e\u0644\u0627\u0642": 0,
"\u062a\u0627\u0631\u06cc\u062e": 1,
"\u062d\u062f\u06cc\u062b": 2,
"\u0633\u06cc\u0627\u0633\u06cc": 3,
"\u0641\u0642\u0647 \u0648 \u0627\u062d\u06a9\u0627\u0645": 4,
"\u0642\u0631\u0622\u0646": 5,
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"\u06a9\u0644\u0627\u0645 \u0648 \u0627\u0639\u062a\u0642\u0627\u062f\u0627\u062a": 7
},
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "xlm-roberta",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"output_past": true,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"problem_type": "single_label_classification",
"tie_word_embeddings": true,
"transformers_version": "5.8.1",
"type_vocab_size": 1,
"use_cache": false,
"vocab_size": 250002
}
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