Text Classification
Transformers
Safetensors
Bengali
English
xlm-roberta
safeage
safety-classification
bangla
banglish
text-embeddings-inference
Instructions to use mehedi4/SafeAge-XLM-R-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mehedi4/SafeAge-XLM-R-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mehedi4/SafeAge-XLM-R-Base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mehedi4/SafeAge-XLM-R-Base") model = AutoModelForSequenceClassification.from_pretrained("mehedi4/SafeAge-XLM-R-Base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 449 Bytes
3fb84d7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"model_source": "FacebookAI/xlm-roberta-base",
"model_revision": "e73636d4f797dec63c3081bb6ed5c7b0bb3f2089",
"training_method": "full_finetune",
"requires_base_model_download": false,
"num_labels": 2,
"id2label": {
"0": "benign",
"1": "attack"
},
"max_length": 512,
"pad_token_id": 1,
"saved_dtype": "float32",
"original_training_precision": "float16_mixed",
"padding_side": "right",
"truncation_side": "right"
}
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