Instructions to use lisaterumi/postagger-portuguese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lisaterumi/postagger-portuguese with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="lisaterumi/postagger-portuguese")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("lisaterumi/postagger-portuguese") model = AutoModelForTokenClassification.from_pretrained("lisaterumi/postagger-portuguese") - Notebooks
- Google Colab
- Kaggle
Commit ·
d249f8f
1
Parent(s): a657405
Delete config.json
Browse files- config.json +0 -90
config.json
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{
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"_name_or_path": "neuralmind/bert-base-portuguese-cased",
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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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"classifier_dropout": null,
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"directionality": "bidi",
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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": "PRO-KS",
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"1": "KC",
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"2": "PDEN",
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"3": "V",
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"4": "PROSUB",
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"5": "PCP",
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"6": "PREP+PRO-KS",
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"7": "PREP+PROSUB",
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"8": "PREP",
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"9": "PU",
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"10": "KS",
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"11": "IN",
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"12": "NPROP",
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"13": "N",
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"14": "PREP+PROPESS",
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"15": "ADV",
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"16": "PREP+ADV",
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"17": "PREP+ART",
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"18": "ADJ",
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"19": "NUM",
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"20": "ART",
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"21": "PROPESS",
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"22": "PROADJ",
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"23": "CUR",
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"24": "PREP+PROADJ",
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"25": "ADV-KS",
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"26": "<pad>"
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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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"<pad>": 26,
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"ADJ": 18,
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"ADV": 15,
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"ADV-KS": 25,
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"ART": 20,
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"CUR": 23,
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"IN": 11,
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"KC": 1,
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"KS": 10,
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"N": 13,
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"NPROP": 12,
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"NUM": 19,
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"PCP": 5,
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"PDEN": 2,
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"PREP": 8,
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"PREP+ADV": 16,
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"PREP+ART": 17,
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"PREP+PRO-KS": 6,
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"PREP+PROADJ": 24,
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"PREP+PROPESS": 14,
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"PREP+PROSUB": 7,
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"PRO-KS": 0,
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"PROADJ": 22,
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"PROPESS": 21,
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"PROSUB": 4,
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"PU": 9,
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"V": 3
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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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"output_past": true,
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"pad_token_id": 0,
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"pooler_fc_size": 768,
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"pooler_num_attention_heads": 12,
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"pooler_num_fc_layers": 3,
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"pooler_size_per_head": 128,
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"pooler_type": "first_token_transform",
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.20.1",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 29794
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}
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