Instructions to use Adignite/query-topic-l2-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Adignite/query-topic-l2-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Adignite/query-topic-l2-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Adignite/query-topic-l2-classifier") model = AutoModelForSequenceClassification.from_pretrained("Adignite/query-topic-l2-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,419 Bytes
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"activation": "gelu",
"architectures": [
"DistilBertForSequenceClassification"
],
"attention_dropout": 0.1,
"bos_token_id": null,
"dim": 768,
"dropout": 0.1,
"dtype": "float32",
"eos_token_id": null,
"hidden_dim": 3072,
"id2label": {
"0": "AI",
"1": "Bollywood",
"2": "Cities",
"3": "Countries",
"4": "Cricket",
"5": "Fitness",
"6": "Football",
"7": "Gadgets",
"8": "General",
"9": "Hollywood",
"10": "India",
"11": "Mental Health",
"12": "Nutrition",
"13": "Olympics",
"14": "Space",
"15": "Tennis",
"16": "UK",
"17": "USA",
"18": "World Wars"
},
"initializer_range": 0.02,
"label2id": {
"AI": 0,
"Bollywood": 1,
"Cities": 2,
"Countries": 3,
"Cricket": 4,
"Fitness": 5,
"Football": 6,
"Gadgets": 7,
"General": 8,
"Hollywood": 9,
"India": 10,
"Mental Health": 11,
"Nutrition": 12,
"Olympics": 13,
"Space": 14,
"Tennis": 15,
"UK": 16,
"USA": 17,
"World Wars": 18
},
"max_position_embeddings": 512,
"model_type": "distilbert",
"n_heads": 12,
"n_layers": 6,
"pad_token_id": 0,
"problem_type": "single_label_classification",
"qa_dropout": 0.1,
"seq_classif_dropout": 0.2,
"sinusoidal_pos_embds": false,
"tie_weights_": true,
"tie_word_embeddings": true,
"transformers_version": "5.0.0",
"vocab_size": 30522
}
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