Instructions to use pnr-svc/topic_classification_model_interpress with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pnr-svc/topic_classification_model_interpress with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pnr-svc/topic_classification_model_interpress")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pnr-svc/topic_classification_model_interpress") model = AutoModelForSequenceClassification.from_pretrained("pnr-svc/topic_classification_model_interpress", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,190 Bytes
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"_name_or_path": "dbmdz/bert-base-turkish-cased",
"architectures": [
"BertForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "k\u00fclt\u00fcrsanat",
"1": "ekonomi",
"2": "siyaset",
"3": "e\u011fitim",
"4": "d\u00fcnya",
"5": "spor",
"6": "teknoloji",
"7": "magazin",
"8": "sa\u011fl\u0131k",
"9": "g\u00fcndem"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"d\u00fcnya": 4,
"ekonomi": 1,
"e\u011fitim": 3,
"g\u00fcndem": 9,
"k\u00fclt\u00fcrsanat": 0,
"magazin": 7,
"sa\u011fl\u0131k": 8,
"siyaset": 2,
"spor": 5,
"teknoloji": 6
},
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"problem_type": "single_label_classification",
"torch_dtype": "float32",
"transformers_version": "4.35.2",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 32000
}
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