Feature Extraction
Transformers
Safetensors
English
cronformer
cron
schedules
text-to-cron
structured-prediction
custom-code
custom_code
Eval Results (legacy)
Instructions to use impalasys/cronformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use impalasys/cronformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="impalasys/cronformer", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("impalasys/cronformer", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,413 Bytes
69c05ab | 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": [
"CronformerModel"
],
"auto_map": {
"AutoConfig": "configuration_cronformer.CronformerConfig",
"AutoModel": "modeling_cronformer.CronformerModel"
},
"base_model": "google/bert_uncased_L-2_H-128_A-2",
"dropout": 0.1,
"dtype": "float32",
"encoder_config": {
"attention_probs_dropout_prob": 0.1,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 128,
"initializer_range": 0.02,
"intermediate_size": 512,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 2,
"num_hidden_layers": 2,
"pad_token_id": 0,
"type_vocab_size": 2,
"vocab_size": 30522
},
"encoder_model": "google/bert_uncased_L-2_H-128_A-2",
"hidden_size": 256,
"input_sequence_length": 128,
"lang_tokenizer": "google/bert_uncased_L-2_H-128_A-2",
"library_name": "transformers",
"list_item_components": null,
"list_item_score_scale": 1.0,
"model_type": "cronformer",
"num_attention_heads": 4,
"num_field_layers": 1,
"token_value_decoding_components": [
0,
1
],
"transformers_version": "4.57.1",
"use_list_item_decoding": false,
"use_list_item_scoring": false,
"use_path_score_decoding": false,
"use_token_value_decoding": false,
"use_value_count_decoding": true,
"value_count_decoding_components": [
0,
1,
2,
3,
4
]
}
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