Instructions to use Quym5124050/vihealthvn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Quym5124050/vihealthvn with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Quym5124050/vihealthvn")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Quym5124050/vihealthvn") model = AutoModelForSequenceClassification.from_pretrained("Quym5124050/vihealthvn", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,804 Bytes
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"architectures": [
"BertForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"directionality": "bidi",
"dtype": "float32",
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "Bi\u1ec3u hi\u1ec7n l\u00e2m s\u00e0ng ch\u01b0a ph\u00e2n lo\u1ea1i.",
"1": "Bi\u1ec3u hi\u1ec7n: nhi\u1ec5m tr\u00f9ng.",
"2": "Bi\u1ec3u hi\u1ec7n: tim m\u1ea1ch.",
"3": "B\u1ec7nh l\u00fd n\u1ed9i khoa m\u00e3n t\u00ednh",
"4": "Ch\u1ea5n th\u01b0\u01a1ng / Ngo\u1ea1i khoa",
"5": "H\u1ed9i ch\u1ee9ng: h\u00f4 h\u1ea5p",
"6": "H\u1ed9i ch\u1ee9ng: ti\u00eau ho\u00e1",
"7": "H\u1ed9i ch\u1ee9ng: ti\u00eau ho\u00e1 tr\u00ean."
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"Bi\u1ec3u hi\u1ec7n l\u00e2m s\u00e0ng ch\u01b0a ph\u00e2n lo\u1ea1i.": 0,
"Bi\u1ec3u hi\u1ec7n: nhi\u1ec5m tr\u00f9ng.": 1,
"Bi\u1ec3u hi\u1ec7n: tim m\u1ea1ch.": 2,
"B\u1ec7nh l\u00fd n\u1ed9i khoa m\u00e3n t\u00ednh": 3,
"Ch\u1ea5n th\u01b0\u01a1ng / Ngo\u1ea1i khoa": 4,
"H\u1ed9i ch\u1ee9ng: h\u00f4 h\u1ea5p": 5,
"H\u1ed9i ch\u1ee9ng: ti\u00eau ho\u00e1": 6,
"H\u1ed9i ch\u1ee9ng: ti\u00eau ho\u00e1 tr\u00ean.": 7
},
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"pooler_fc_size": 768,
"pooler_num_attention_heads": 12,
"pooler_num_fc_layers": 3,
"pooler_size_per_head": 128,
"pooler_type": "first_token_transform",
"position_embedding_type": "absolute",
"problem_type": "single_label_classification",
"transformers_version": "4.57.3",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 119547
}
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