Text Classification
Transformers
PyTorch
Catalan
roberta
catalan
text classification
WikiCAT_ca
CaText
Catalan Textual Corpus
Eval Results (legacy)
text-embeddings-inference
Instructions to use projecte-aina/roberta-base-ca-v2-cased-wikicat-ca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use projecte-aina/roberta-base-ca-v2-cased-wikicat-ca with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="projecte-aina/roberta-base-ca-v2-cased-wikicat-ca")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("projecte-aina/roberta-base-ca-v2-cased-wikicat-ca") model = AutoModelForSequenceClassification.from_pretrained("projecte-aina/roberta-base-ca-v2-cased-wikicat-ca", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from projecte-aina/roberta-base-ca-v2-cased-wikicat-ca: direct link, hf CLI and curl.
- Browser
- Download file 1.52 kB
-
https://huggingface.co/projecte-aina/roberta-base-ca-v2-cased-wikicat-ca/resolve/main/config.json
- Command line
-
hf download hf://projecte-aina/roberta-base-ca-v2-cased-wikicat-ca/config.json
-
curl -L -o config.json https://huggingface.co/projecte-aina/roberta-base-ca-v2-cased-wikicat-ca/resolve/main/config.json
1.52 kB
| { | |
| "_name_or_path": "/gpfs/projects/bsc88/projects/catalan_evaluation/models/roberta-base-ca-v2", | |
| "architectures": [ | |
| "RobertaForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "eos_token_id": 2, | |
| "finetuning_task": "wikicat", | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "Ci\u00e8ncia_i_Tecnologia", | |
| "1": "Dret", | |
| "2": "Economia", | |
| "3": "Enginyeria", | |
| "4": "Entreteniment", | |
| "5": "Esport", | |
| "6": "Filosofia", | |
| "7": "Hist\u00f2ria", | |
| "8": "Humanitats", | |
| "9": "Matem\u00e0tiques", | |
| "10": "M\u00fasica", | |
| "11": "Pol\u00edtica", | |
| "12": "Religi\u00f3" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "Ci\u00e8ncia_i_Tecnologia": 0, | |
| "Dret": 1, | |
| "Economia": 2, | |
| "Enginyeria": 3, | |
| "Entreteniment": 4, | |
| "Esport": 5, | |
| "Filosofia": 6, | |
| "Hist\u00f2ria": 7, | |
| "Humanitats": 8, | |
| "Matem\u00e0tiques": 9, | |
| "M\u00fasica": 10, | |
| "Pol\u00edtica": 11, | |
| "Religi\u00f3": 12 | |
| }, | |
| "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": "4.17.0", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 50262 | |
| } | |