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: 689 Bytes
3850802 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | {
"add_cross_attention": false,
"architectures": [
"RobertaForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"dtype": "float32",
"eos_token_id": 2,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"is_decoder": false,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "roberta",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 1,
"tie_word_embeddings": true,
"transformers_version": "5.0.0",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 50265
}
|