Instructions to use KevinGeertjens/bert-classification-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KevinGeertjens/bert-classification-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="KevinGeertjens/bert-classification-model", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("KevinGeertjens/bert-classification-model", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 751 Bytes
690ab37 2868a91 690ab37 91c61bf 690ab37 | 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 | {
"architectures": [
"BertClassificationModel"
],
"attention_probs_dropout_prob": 0.1,
"auto_map": {
"AutoModel": "bert_classification_model.BertClassificationModel"
},
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert-classification",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"num_main_segment": 0,
"num_sub_segment": 0,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"torch_dtype": "float32",
"transformers_version": "4.22.0",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
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