Text Classification
Transformers
ONNX
Safetensors
English
Portuguese
modernbert
feature-extraction
typestrict
decision-model
typed-decisions
encoder
decision-index
cpu
text-embeddings-inference
Instructions to use typestrict/TS-moBERTo-150M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use typestrict/TS-moBERTo-150M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="typestrict/TS-moBERTo-150M")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("typestrict/TS-moBERTo-150M") model = AutoModel.from_pretrained("typestrict/TS-moBERTo-150M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 339 Bytes
9817fc9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 | {
"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
}
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