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
| { | |
| "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 | |
| ] | |
| } | |