Token Classification
Transformers
ONNX
Safetensors
English
Japanese
Chinese
bert
anime
filename-parsing
Eval Results (legacy)
Instructions to use ModerRAS/AniFileBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModerRAS/AniFileBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ModerRAS/AniFileBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ModerRAS/AniFileBERT") model = AutoModelForTokenClassification.from_pretrained("ModerRAS/AniFileBERT") - Notebooks
- Google Colab
- Kaggle
File size: 851 Bytes
e63569d | 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 | [project]
name = "anifilebert"
version = "0.1.0"
description = "Tiny BERT token-classification model and tooling for parsing anime release filenames."
readme = "README.md"
requires-python = ">=3.11"
license = { text = "Apache-2.0" }
dependencies = [
"accelerate==1.13.0",
"datasets==4.8.5",
"numpy==2.4.5",
"onnx==1.21.0",
"onnxruntime==1.26.0",
"onnxscript==0.7.0",
"seqeval==1.2.2",
"tensorboard>=2.14.0",
"torch==2.12.0+cu126",
"transformers==5.8.1",
]
[project.urls]
Repository = "https://huggingface.co/ModerRAS/AniFileBERT"
[tool.uv]
package = false
environments = ["sys_platform == 'win32'"]
[tool.uv.sources]
torch = [
{ index = "pytorch-cu126", marker = "platform_system == 'Windows'" },
]
[[tool.uv.index]]
name = "pytorch-cu126"
url = "https://download.pytorch.org/whl/cu126"
explicit = true
|