Instructions to use dominguesm/canarim-bert-nheengatu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dominguesm/canarim-bert-nheengatu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dominguesm/canarim-bert-nheengatu")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dominguesm/canarim-bert-nheengatu") model = AutoModelForMaskedLM.from_pretrained("dominguesm/canarim-bert-nheengatu", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from dominguesm/canarim-bert-nheengatu: direct link, hf CLI and curl.
- Browser
- Download file 638 Bytes
-
https://huggingface.co/dominguesm/canarim-bert-nheengatu/resolve/54f1e9b577cecbef5c720043c1fc9dbb72c55806/config.json
- Command line
-
hf download hf://dominguesm/canarim-bert-nheengatu@54f1e9b577cecbef5c720043c1fc9dbb72c55806/config.json
-
curl -L -o config.json https://huggingface.co/dominguesm/canarim-bert-nheengatu/resolve/54f1e9b577cecbef5c720043c1fc9dbb72c55806/config.json
638 Bytes
| { | |
| "_name_or_path": "data/run_20231227-224314/model", | |
| "architectures": [ | |
| "BertForMaskedLM" | |
| ], | |
| "attention_probs_dropout_prob": 0, | |
| "classifier_dropout": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 128, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.36.2", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 32768 | |
| } | |