Instructions to use contemmcm/3e178a39e286163561f9a670e11465a1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use contemmcm/3e178a39e286163561f9a670e11465a1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="contemmcm/3e178a39e286163561f9a670e11465a1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("contemmcm/3e178a39e286163561f9a670e11465a1") model = AutoModelForSequenceClassification.from_pretrained("contemmcm/3e178a39e286163561f9a670e11465a1") - Notebooks
- Google Colab
- Kaggle
File size: 1,229 Bytes
cfd7e40 | 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 48 49 50 51 52 53 54 55 56 | {
"architectures": [
"AlbertForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 2,
"classifier_dropout_prob": 0.1,
"down_scale_factor": 1,
"dtype": "float32",
"embedding_size": 128,
"eos_token_id": 3,
"gap_size": 0,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 1024,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1",
"2": "LABEL_2",
"3": "LABEL_3",
"4": "LABEL_4",
"5": "LABEL_5",
"6": "LABEL_6",
"7": "LABEL_7",
"8": "LABEL_8"
},
"initializer_range": 0.02,
"inner_group_num": 1,
"intermediate_size": 4096,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1,
"LABEL_2": 2,
"LABEL_3": 3,
"LABEL_4": 4,
"LABEL_5": 5,
"LABEL_6": 6,
"LABEL_7": 7,
"LABEL_8": 8
},
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "albert",
"net_structure_type": 0,
"num_attention_heads": 16,
"num_hidden_groups": 1,
"num_hidden_layers": 24,
"num_memory_blocks": 0,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"problem_type": "single_label_classification",
"transformers_version": "4.57.0",
"type_vocab_size": 2,
"vocab_size": 30000
}
|