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-virtual-sps32-10epoch-lightfocus", | |
| "data_file": "data/generated/focus_after_virtual_sps32_char.jsonl", | |
| "data_sources": [ | |
| { | |
| "role": "primary", | |
| "path": "data/generated/focus_after_virtual_sps32_char.jsonl", | |
| "samples": 140660, | |
| "repeat": 1, | |
| "effective_samples": 140660 | |
| } | |
| ], | |
| "augmentation": { | |
| "partial_requested": 0, | |
| "partial_written": 0, | |
| "permutation_requested": 0, | |
| "permutation_written": 0, | |
| "special_requested": 0, | |
| "special_written": 0, | |
| "max_chars": 160 | |
| }, | |
| "dataset_mode": "encoded", | |
| "virtual_dataset_dir": null, | |
| "apply_label_repairs": false, | |
| "keep_raw_dataset": false, | |
| "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": 133627, | |
| "eval_samples": 7033, | |
| "load_seconds": 3.860345099994447, | |
| "encode_seconds": 11.22450440004468, | |
| "epochs": 1.0, | |
| "max_steps": -1, | |
| "batch_size": 1792, | |
| "learning_rate": 2e-06, | |
| "warmup_steps": 20, | |
| "seed": 208, | |
| "device": "cuda", | |
| "fp16": false, | |
| "gradient_accumulation_steps": 1, | |
| "dataloader_num_workers": 0, | |
| "dataloader_prefetch_factor": null, | |
| "dataloader_persistent_workers": false, | |
| "dataloader_pin_memory": true, | |
| "encoded_dataset_device": "cpu", | |
| "mixed_precision": "bf16", | |
| "tf32": true, | |
| "torch_compile": false, | |
| "auto_find_batch_size": false, | |
| "perf_log_steps": 50, | |
| "perf_sample_interval": 0.5, | |
| "periodic_eval": false | |
| } |