Instructions to use vblagoje/bert-english-uncased-finetuned-pos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vblagoje/bert-english-uncased-finetuned-pos with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="vblagoje/bert-english-uncased-finetuned-pos")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("vblagoje/bert-english-uncased-finetuned-pos") model = AutoModelForTokenClassification.from_pretrained("vblagoje/bert-english-uncased-finetuned-pos") - Inference
- Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +58 -0
config.json
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{
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"architectures": [
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"BertForTokenClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "ADJ",
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"1": "ADP",
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"2": "ADV",
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"3": "AUX",
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"4": "CCONJ",
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"5": "DET",
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"6": "INTJ",
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"7": "NOUN",
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"8": "NUM",
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"9": "PART",
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"10": "PRON",
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"11": "PROPN",
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"12": "PUNCT",
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"13": "SCONJ",
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"14": "SYM",
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"15": "VERB",
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"16": "X"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"ADJ": 0,
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"ADP": 1,
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"ADV": 2,
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"AUX": 3,
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"CCONJ": 4,
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"DET": 5,
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"INTJ": 6,
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"NOUN": 7,
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"NUM": 8,
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"PART": 9,
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"PRON": 10,
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"PROPN": 11,
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"PUNCT": 12,
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"SCONJ": 13,
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"SYM": 14,
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"VERB": 15,
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"X": 16
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"type_vocab_size": 2,
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"vocab_size": 30522
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}
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