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
| { | |
| "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, | |
| "\u0645\u0634\u0627\u0648\u0631\u0647": 6, | |
| "\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 | |
| } | |