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
| { | |
| "experiment_name": "dmhy-char-full-relabel", | |
| "data_file": "datasets/AnimeName/dmhy_weak_char.jsonl", | |
| "tokenizer_variant": "char", | |
| "vocab_file": "datasets/AnimeName/vocab.char.json", | |
| "vocab_size": 6199, | |
| "max_seq_length": 128, | |
| "hidden_size": 256, | |
| "num_hidden_layers": 4, | |
| "num_attention_heads": 8, | |
| "intermediate_size": 1024, | |
| "train_samples": 619361, | |
| "eval_samples": 12641, | |
| "epochs": 2.0, | |
| "batch_size": 256, | |
| "learning_rate": 8e-05, | |
| "warmup_steps": 300, | |
| "seed": 48, | |
| "device": "cuda", | |
| "fp16": true, | |
| "gradient_accumulation_steps": 1, | |
| "dataloader_num_workers": 4 | |
| } |