Text Classification
Transformers
Safetensors
Tswana
roberta
offensive-language-detection
setswana
low-resource-nlp
digital-forensics
explainable-ai
rationale-learning
masked-rationale-prediction
puoberta
lime
s-lime
text-embeddings-inference
Instructions to use mopatik/PuoBERTa_MRP_version with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mopatik/PuoBERTa_MRP_version with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mopatik/PuoBERTa_MRP_version")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mopatik/PuoBERTa_MRP_version") model = AutoModelForSequenceClassification.from_pretrained("mopatik/PuoBERTa_MRP_version") - Notebooks
- Google Colab
- Kaggle
File size: 386 Bytes
3850802 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"add_prefix_space": false,
"backend": "tokenizers",
"bos_token": "<s>",
"cls_token": "<s>",
"eos_token": "</s>",
"errors": "replace",
"is_local": true,
"mask_token": "<mask>",
"model_max_length": 1000000000000000019884624838656,
"pad_token": "<pad>",
"sep_token": "</s>",
"tokenizer_class": "RobertaTokenizer",
"trim_offsets": true,
"unk_token": "<unk>"
}
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