Fill-Mask
Transformers
Safetensors
Ancient Greek (to 1453)
char_bert
ancient-greek
classical-philology
character-level
masked-diffusion
pretrained
custom_code
Instructions to use Ericu950/Stoicheia-doc_clean with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ericu950/Stoicheia-doc_clean with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Ericu950/Stoicheia-doc_clean", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Ericu950/Stoicheia-doc_clean", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,114 Bytes
2643cac | 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 | """HF-Hub-compatible config for Stoicheia (CharBertEncoder)."""
from transformers import PretrainedConfig
class CharBertConfig(PretrainedConfig):
model_type = "char_bert"
def __init__(
self,
n_alpha: int = 24,
mask_id: int = 24,
blank_id: int = 25,
pad_id: int = 26,
n_char_ids: int = 27,
n_boundary: int = 4,
n_dia: int = 49,
n_punct: int = 7,
d_model: int = 1024,
n_heads: int = 16,
depth: int = 32,
char_window: int = 256,
attn_impl: str = "sdpa",
qk_norm: bool = True,
**kwargs,
):
self.n_alpha = n_alpha
self.mask_id = mask_id
self.blank_id = blank_id
self.pad_id = pad_id
self.n_char_ids = n_char_ids
self.n_boundary = n_boundary
self.n_dia = n_dia
self.n_punct = n_punct
self.d_model = d_model
self.n_heads = n_heads
self.depth = depth
self.char_window = char_window
self.attn_impl = attn_impl
self.qk_norm = qk_norm
super().__init__(**kwargs)
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