Instructions to use rosimeirecosta/roberta-base-cased-pt-c-corpus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rosimeirecosta/roberta-base-cased-pt-c-corpus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="rosimeirecosta/roberta-base-cased-pt-c-corpus")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("rosimeirecosta/roberta-base-cased-pt-c-corpus") model = AutoModelForMaskedLM.from_pretrained("rosimeirecosta/roberta-base-cased-pt-c-corpus") - Notebooks
- Google Colab
- Kaggle
File size: 760 Bytes
3e3930b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | {
"_name_or_path": "/content/drive/MyDrive/lm-ccorpus_roberta-RobertaTwitterBR/model/",
"architectures": [
"RobertaForMaskedLM"
],
"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,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 514,
"model_type": "roberta",
"num_attention_heads": 12,
"num_hidden_layers": 6,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"torch_dtype": "float32",
"transformers_version": "4.24.0",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 150000
}
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