Text Classification
Transformers
ONNX
Safetensors
English
Portuguese
modernbert
feature-extraction
encoder
typed-decisions
decision-index
tool-use
cpu
decision-model
text-embeddings-inference
Instructions to use Lukitaduarte/dinah-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lukitaduarte/dinah-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Lukitaduarte/dinah-1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Lukitaduarte/dinah-1") model = AutoModel.from_pretrained("Lukitaduarte/dinah-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from Lukitaduarte/dinah-1: direct link, hf CLI and curl.
- Browser
- Download file 339 Bytes
-
https://huggingface.co/Lukitaduarte/dinah-1/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://Lukitaduarte/dinah-1/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Lukitaduarte/dinah-1/resolve/main/tokenizer_config.json
339 Bytes
| { | |
| "tokenizer_class": "PreTrainedTokenizerFast", | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "sep_token": "[SEP]", | |
| "pad_token": "[PAD]", | |
| "mask_token": "[MASK]", | |
| "unk_token": "[UNK]", | |
| "additional_special_tokens": ["[OPT]"], | |
| "model_input_names": ["input_ids", "attention_mask"], | |
| "model_max_length": 8192 | |
| } | |