Token Classification
Transformers
ONNX
Safetensors
English
Japanese
Chinese
bert
anime
filename-parsing
Instructions to use chivehao/AniFileBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chivehao/AniFileBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="chivehao/AniFileBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("chivehao/AniFileBERT") model = AutoModelForTokenClassification.from_pretrained("chivehao/AniFileBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download run_metadata.json from chivehao/AniFileBERT: direct link, hf CLI and curl.
- Browser
- Download file 602 Bytes
-
https://huggingface.co/chivehao/AniFileBERT/resolve/main/run_metadata.json
- Command line
-
hf download hf://chivehao/AniFileBERT/run_metadata.json
-
curl -L -o run_metadata.json https://huggingface.co/chivehao/AniFileBERT/resolve/main/run_metadata.json
602 Bytes
| { | |
| "experiment_name": "dmhy-char-guoman-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": 52, | |
| "device": "cuda", | |
| "fp16": true, | |
| "gradient_accumulation_steps": 1, | |
| "dataloader_num_workers": 4 | |
| } |