Instructions to use juliushanusch/ChronosBoltBERT-Test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use juliushanusch/ChronosBoltBERT-Test with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, ChronosBoltModelForForecasting tokenizer = AutoTokenizer.from_pretrained("juliushanusch/ChronosBoltBERT-Test") model = ChronosBoltModelForForecasting.from_pretrained("juliushanusch/ChronosBoltBERT-Test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,355 Bytes
3962289 | 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 57 | {
"architectures": [
"ChronosBoltModelForForecasting"
],
"attention_probs_dropout_prob": 0.1,
"chronos_config": {
"context_length": 4096,
"debug_batch_idx": 0,
"debug_max_patches": 3,
"debug_patching": false,
"individual_dropout": 0.05,
"input_patch_size": 16,
"input_patch_stride": 16,
"masking_prob": 0.15,
"model_id": "google-bert/bert-base-cased",
"patch_masking_prob": 0.8,
"patch_perturbation_noise_strength": 0.1,
"patch_perturbation_prob": 0.1,
"patch_perturbation_scale_strength": 0.1,
"prediction_length": 1,
"quantiles": [
0.1,
0.2,
0.3,
0.4,
0.5,
0.6,
0.7,
0.8,
0.9
],
"unchanged_patch_prob": 0.1,
"use_reg_token": false
},
"classifier_dropout": null,
"dtype": "float32",
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.05,
"intermediate_size": 3072,
"layer_norm_eps": 1e-12,
"mask_token_id": 1,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"tie_word_embeddings": false,
"transformers_version": "4.57.6",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 2
}
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