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: 515 Bytes
3fb84d7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"versions": {
"torch": "2.11.0+cu128",
"transformers": "5.5.4",
"accelerate": "1.13.0",
"numpy": "2.1.3",
"pandas": "2.3.3",
"scikit-learn": "1.6.1",
"tokenizers": "0.22.2",
"huggingface-hub": "1.29.0"
},
"cuda_version": "12.8",
"world_size": 2,
"effective_batch_size": 32,
"model_revision": "e73636d4f797dec63c3081bb6ed5c7b0bb3f2089",
"model_path": "/kaggle/working/hf_cache/models--FacebookAI--xlm-roberta-base/snapshots/e73636d4f797dec63c3081bb6ed5c7b0bb3f2089"
}
|