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
| { | |
| "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>" | |
| } | |