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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ModerRAS/AniFileBERT") model = AutoModelForTokenClassification.from_pretrained("ModerRAS/AniFileBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,864 Bytes
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"experiment_name": "ablation-schema-v2-hardfocus-cache-repaired-from-baseline-seed62-10epoch-rerun",
"data_file": "data\\schema_v2_hard_focus_char_seed63.jsonl",
"data_sources": [
{
"role": "encoded_cache",
"path": "data\\encoded_cache\\schema_v2_hard_focus_char_seed63_split995_repaired_v2",
"samples": 1074262,
"repeat": 1,
"effective_samples": 1074262
}
],
"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-cache-sharded",
"encoded_cache_dir": "data\\encoded_cache\\schema_v2_hard_focus_char_seed63_split995_repaired_v2",
"virtual_dataset_dir": null,
"apply_label_repairs": false,
"keep_raw_dataset": false,
"tokenizer_variant": "char",
"model_head": "linear",
"train_crf_only": false,
"rdrop_alpha": 0.0,
"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": 1068890,
"eval_samples": 5372,
"load_seconds": 0.20706980000022668,
"encode_seconds": 0.19879850000006627,
"epochs": 10.0,
"max_steps": -1,
"batch_size": 512,
"learning_rate": 4e-05,
"warmup_steps": 120,
"seed": 63,
"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": true,
"perf_log_steps": 100,
"perf_sample_interval": 1.0,
"periodic_eval": false
} |