Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JonatanGk/roberta-base-ca-finetuned-tecla with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JonatanGk/roberta-base-ca-finetuned-tecla with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JonatanGk/roberta-base-ca-finetuned-tecla")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JonatanGk/roberta-base-ca-finetuned-tecla") model = AutoModelForSequenceClassification.from_pretrained("JonatanGk/roberta-base-ca-finetuned-tecla", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "BSC-TeMU/roberta-base-ca", | |
| "architectures": [ | |
| "RobertaForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "eos_token_id": 2, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "Societat", | |
| "1": "Pol铆tica", | |
| "2": "Partits", | |
| "3": "Successos", | |
| "4": "Judicial", | |
| "5": "Policial", | |
| "6": "Salut", | |
| "7": "Economia", | |
| "8": "Parlament", | |
| "9": "Medi_ambient", | |
| "10": "M煤sica", | |
| "11": "Educaci贸", | |
| "12": "Empresa", | |
| "13": "Cultura", | |
| "14": "Uni贸_Europea", | |
| "15": "Govern", | |
| "16": "Infraestructures", | |
| "17": "Treball", | |
| "18": "Mobilitat", | |
| "19": "Cinema", | |
| "20": "Teatre", | |
| "21": "Turisme", | |
| "22": "Equipaments_i_patrimoni", | |
| "23": "Lletres", | |
| "24": "Meteorologia", | |
| "25": "Comer莽", | |
| "26": "Govern_espanyol", | |
| "27": "M贸n", | |
| "28": "Festa_i_cultura_popular", | |
| "29": "Tr脿nsit" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "Societat": 0, | |
| "Pol铆tica": 1, | |
| "M煤sica": 10, | |
| "Educaci贸": 11, | |
| "Empresa": 12, | |
| "Cultura": 13, | |
| "Uni贸_Europea": 14, | |
| "Govern": 15, | |
| "Infraestructures": 16, | |
| "Treball": 17, | |
| "Mobilitat": 18, | |
| "Cinema": 19, | |
| "Partits": 2, | |
| "Teatre": 20, | |
| "Turisme": 21, | |
| "Equipaments_i_patrimoni": 22, | |
| "Lletres": 23, | |
| "Meteorologia": 24, | |
| "Comer莽": 25, | |
| "Govern_espanyol": 26, | |
| "M贸n": 27, | |
| "Festa_i_cultura_popular": 28, | |
| "Tr脿nsit": 29, | |
| "Successos": 3, | |
| "Judicial": 4, | |
| "Policial": 5, | |
| "Salut": 6, | |
| "Economia": 7, | |
| "Parlament": 8, | |
| "Medi_ambient": 9 | |
| }, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 514, | |
| "model_type": "roberta", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "problem_type": "single_label_classification", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.11.3", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 52000 | |
| } | |