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
Download reports/run_metadata.json from ModerRAS/AniFileBERT: direct link, hf CLI and curl.
- Browser
- Download file 1.86 kB
-
https://huggingface.co/ModerRAS/AniFileBERT/resolve/main/reports/run_metadata.json
- Command line
-
hf download hf://ModerRAS/AniFileBERT/reports/run_metadata.json
-
curl -L -o run_metadata.json https://huggingface.co/ModerRAS/AniFileBERT/resolve/main/reports/run_metadata.json
1.86 kB
| { | |
| "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 | |
| } |