Instructions to use cscscsds/ddp_test_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cscscsds/ddp_test_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cscscsds/ddp_test_3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cscscsds/ddp_test_3") model = AutoModelForSequenceClassification.from_pretrained("cscscsds/ddp_test_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,168 Bytes
4dad352 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | {
"_name_or_path": "beomi/KcELECTRA-base-v2022",
"architectures": [
"ElectraForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"embedding_size": 768,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "\ubd84\ub178",
"1": "\uc2ac\ud514",
"2": "\ubd88\uc548",
"3": "\uc0c1\ucc98",
"4": "\uae30\uc068"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"\uae30\uc068": 4,
"\ubd84\ub178": 0,
"\ubd88\uc548": 2,
"\uc0c1\ucc98": 3,
"\uc2ac\ud514": 1
},
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "electra",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"problem_type": "single_label_classification",
"summary_activation": "gelu",
"summary_last_dropout": 0.1,
"summary_type": "first",
"summary_use_proj": true,
"tokenizer_class": "BertTokenizer",
"torch_dtype": "float32",
"transformers_version": "4.34.0",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 54343
}
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