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
File size: 1,392 Bytes
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"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": 64,
"model_type": "bert",
"num_attention_heads": 8,
"num_hidden_layers": 4,
"pad_token_id": 0,
"tie_word_embeddings": true,
"tokenizer_variant": "regex",
"transformers_version": "5.8.0",
"type_vocab_size": 2,
"use_cache": false,
"vocab_size": 3000
}
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