Token Classification
Transformers
Safetensors
English
Portuguese
xlm-roberta
named-entity-recognition
metadata-extraction
bert
meeting-minutes
municipal-documents
Instructions to use anonymous13542/XLMR-large-metadata-council-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anonymous13542/XLMR-large-metadata-council-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="anonymous13542/XLMR-large-metadata-council-en")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("anonymous13542/XLMR-large-metadata-council-en") model = AutoModelForTokenClassification.from_pretrained("anonymous13542/XLMR-large-metadata-council-en") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "XLMRobertaForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "eos_token_id": 2, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1024, | |
| "id2label": { | |
| "0": "O", | |
| "1": "B-DATA", | |
| "2": "I-DATA", | |
| "3": "B-HORARIO-INICIO", | |
| "4": "I-HORARIO-INICIO", | |
| "5": "B-HORARIO-FIM", | |
| "6": "I-HORARIO-FIM", | |
| "7": "B-LOCAL", | |
| "8": "I-LOCAL", | |
| "9": "B-TIPO-REUNIAO-EXTRAORDINARIA", | |
| "10": "I-TIPO-REUNIAO-EXTRAORDINARIA", | |
| "11": "B-TIPO-REUNIAO-ORDINARIA", | |
| "12": "I-TIPO-REUNIAO-ORDINARIA", | |
| "13": "B-NUMERO-ATA", | |
| "14": "I-NUMERO-ATA", | |
| "15": "B-PARTICIPANTE-PRESIDENTE-PRESENTE", | |
| "16": "I-PARTICIPANTE-PRESIDENTE-PRESENTE", | |
| "17": "B-PARTICIPANTE-PRESIDENTE-AUSENTE", | |
| "18": "I-PARTICIPANTE-PRESIDENTE-AUSENTE", | |
| "19": "B-PARTICIPANTE-PRESIDENTE-SUBSTITUIDO", | |
| "20": "I-PARTICIPANTE-PRESIDENTE-SUBSTITUIDO", | |
| "21": "B-PARTICIPANTE-VEREADOR-PRESENTE", | |
| "22": "I-PARTICIPANTE-VEREADOR-PRESENTE", | |
| "23": "B-PARTICIPANTE-VEREADOR-AUSENTE", | |
| "24": "I-PARTICIPANTE-VEREADOR-AUSENTE", | |
| "25": "B-PARTICIPANTE-VEREADOR-SUBSTITUIDO", | |
| "26": "I-PARTICIPANTE-VEREADOR-SUBSTITUIDO" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "label2id": { | |
| "B-DATA": 1, | |
| "B-HORARIO-FIM": 5, | |
| "B-HORARIO-INICIO": 3, | |
| "B-LOCAL": 7, | |
| "B-NUMERO-ATA": 13, | |
| "B-PARTICIPANTE-PRESIDENTE-AUSENTE": 17, | |
| "B-PARTICIPANTE-PRESIDENTE-PRESENTE": 15, | |
| "B-PARTICIPANTE-PRESIDENTE-SUBSTITUIDO": 19, | |
| "B-PARTICIPANTE-VEREADOR-AUSENTE": 23, | |
| "B-PARTICIPANTE-VEREADOR-PRESENTE": 21, | |
| "B-PARTICIPANTE-VEREADOR-SUBSTITUIDO": 25, | |
| "B-TIPO-REUNIAO-EXTRAORDINARIA": 9, | |
| "B-TIPO-REUNIAO-ORDINARIA": 11, | |
| "I-DATA": 2, | |
| "I-HORARIO-FIM": 6, | |
| "I-HORARIO-INICIO": 4, | |
| "I-LOCAL": 8, | |
| "I-NUMERO-ATA": 14, | |
| "I-PARTICIPANTE-PRESIDENTE-AUSENTE": 18, | |
| "I-PARTICIPANTE-PRESIDENTE-PRESENTE": 16, | |
| "I-PARTICIPANTE-PRESIDENTE-SUBSTITUIDO": 20, | |
| "I-PARTICIPANTE-VEREADOR-AUSENTE": 24, | |
| "I-PARTICIPANTE-VEREADOR-PRESENTE": 22, | |
| "I-PARTICIPANTE-VEREADOR-SUBSTITUIDO": 26, | |
| "I-TIPO-REUNIAO-EXTRAORDINARIA": 10, | |
| "I-TIPO-REUNIAO-ORDINARIA": 12, | |
| "O": 0 | |
| }, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 514, | |
| "model_type": "xlm-roberta", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "output_past": true, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.54.0", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 250002 | |
| } | |