Instructions to use Kate-lf/emotion-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kate-lf/emotion-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kate-lf/emotion-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kate-lf/emotion-classification") model = AutoModelForSequenceClassification.from_pretrained("Kate-lf/emotion-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,312 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": "\u4f24\u5fc3",
"1": "\u5173\u5fc3",
"2": "\u538c\u6076",
"3": "\u5e73\u9759",
"4": "\u60ca\u8bb6",
"5": "\u5f00\u5fc3",
"6": "\u751f\u6c14",
"7": "\u7591\u95ee"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"\u4f24\u5fc3": 0,
"\u5173\u5fc3": 1,
"\u538c\u6076": 2,
"\u5e73\u9759": 3,
"\u5f00\u5fc3": 5,
"\u60ca\u8bb6": 4,
"\u751f\u6c14": 6,
"\u7591\u95ee": 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": 21128
}
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