Instructions to use juliushanusch/ChronosBERT-Default with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use juliushanusch/ChronosBERT-Default with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="juliushanusch/ChronosBERT-Default")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("juliushanusch/ChronosBERT-Default") model = AutoModelForMaskedLM.from_pretrained("juliushanusch/ChronosBERT-Default", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,214 Bytes
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"_name_or_path": "prajjwal1/bert-small",
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 1,
"chronos_config": {
"bos_token_id": 1,
"context_length": 512,
"eos_token_id": 2,
"mask_token_id": 3,
"model_type": "mlm",
"n_special_tokens": 4,
"n_tokens": 4096,
"num_samples": 20,
"pad_token_id": 0,
"prediction_length": 64,
"temperature": 1.0,
"tokenizer_class": "MeanScaleUniformBins",
"tokenizer_kwargs": {
"high_limit": 15.0,
"low_limit": -15.0
},
"top_k": 50,
"top_p": 1.0,
"use_eos_token": true
},
"classifier_dropout": null,
"eos_token_id": 2,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 512,
"initializer_range": 0.05,
"intermediate_size": 2048,
"layer_norm_eps": 1e-12,
"mask_token_id": 3,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 8,
"num_hidden_layers": 4,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"tie_word_embeddings": false,
"torch_dtype": "float32",
"transformers_version": "4.49.0",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 4096
}
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