Instructions to use hur03/capturemate-category-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hur03/capturemate-category-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hur03/capturemate-category-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hur03/capturemate-category-classifier") model = AutoModelForSequenceClassification.from_pretrained("hur03/capturemate-category-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,032 Bytes
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"add_cross_attention": false,
"architectures": [
"RobertaForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"dtype": "float32",
"eos_token_id": 2,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "schedule",
"1": "shopping",
"2": "place",
"3": "memo",
"4": "trash"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"is_decoder": false,
"label2id": {
"memo": 3,
"place": 2,
"schedule": 0,
"shopping": 1,
"trash": 4
},
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "roberta",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 1,
"problem_type": "single_label_classification",
"tie_word_embeddings": true,
"tokenizer_class": "BertTokenizer",
"transformers_version": "5.5.4",
"type_vocab_size": 1,
"use_cache": false,
"vocab_size": 32000
}
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