Fill-Mask
Transformers
Safetensors
English
theo_bert_base
masked-language-modeling
bible
theology
christianity
trust-remote-code
custom_code
Eval Results (legacy)
Instructions to use toranb/theo-bert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use toranb/theo-bert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="toranb/theo-bert-base", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("toranb/theo-bert-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 892 Bytes
a64c547 | 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 | {
"release_stage": "mlmcontinued (stage 2, epoch 25)",
"reported_loss": 0.8958267427277438,
"parameter_count": 273051864,
"fp16_export": true,
"tokenizer": "google-bert/bert-base-uncased",
"pretraining_stages": [
{
"stage": 1,
"name": "encoder",
"objective": "token-level masked language modeling at 20% mask rate 80/10/10 split",
"epochs": 24,
"seq_len": 256,
"final_train_loss": 1.0678829201169648,
"final_train_accuracy": 76.41802635495705
},
{
"stage": 2,
"name": "mlmcontinued",
"objective": "whole-word-masking continued pretraining at 18% mask rate",
"epochs": 25,
"seq_len": 256,
"final_train_loss": 0.8958267427277438,
"final_train_accuracy": 79.66191907459469
}
],
"mlm_eval_overall_pass_rate": 0.947,
"mlm_eval_passed_cases": 517,
"mlm_eval_total_cases": 546
}
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