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
| { | |
| "add_cross_attention": false, | |
| "architectures": [ | |
| "BertForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": null, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "eos_token_id": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 256, | |
| "id2label": { | |
| "0": "O", | |
| "1": "B-TITLE", | |
| "2": "I-TITLE", | |
| "3": "B-SEASON", | |
| "4": "I-SEASON", | |
| "5": "B-EPISODE", | |
| "6": "I-EPISODE", | |
| "7": "B-SPECIAL", | |
| "8": "I-SPECIAL", | |
| "9": "B-GROUP", | |
| "10": "I-GROUP", | |
| "11": "B-RESOLUTION", | |
| "12": "I-RESOLUTION", | |
| "13": "B-SOURCE", | |
| "14": "I-SOURCE" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1024, | |
| "is_decoder": false, | |
| "label2id": { | |
| "B-EPISODE": 5, | |
| "B-GROUP": 9, | |
| "B-RESOLUTION": 11, | |
| "B-SEASON": 3, | |
| "B-SOURCE": 13, | |
| "B-SPECIAL": 7, | |
| "B-TITLE": 1, | |
| "I-EPISODE": 6, | |
| "I-GROUP": 10, | |
| "I-RESOLUTION": 12, | |
| "I-SEASON": 4, | |
| "I-SOURCE": 14, | |
| "I-SPECIAL": 8, | |
| "I-TITLE": 2, | |
| "O": 0 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 128, | |
| "max_seq_length": 128, | |
| "model_type": "bert", | |
| "num_attention_heads": 8, | |
| "num_hidden_layers": 4, | |
| "pad_token_id": 0, | |
| "tie_word_embeddings": true, | |
| "tokenizer_variant": "char", | |
| "transformers_version": "5.8.1", | |
| "type_vocab_size": 2, | |
| "use_cache": false, | |
| "vocab_size": 6199 | |
| } | |