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
Download provenance/training_configuration.json from mehedi4/SafeAge-XLM-R-Base: direct link, hf CLI and curl.
- Browser
- Download file 548 Bytes
-
https://huggingface.co/mehedi4/SafeAge-XLM-R-Base/resolve/main/provenance/training_configuration.json
- Command line
-
hf download hf://mehedi4/SafeAge-XLM-R-Base/provenance/training_configuration.json
-
curl -L -o training_configuration.json https://huggingface.co/mehedi4/SafeAge-XLM-R-Base/resolve/main/provenance/training_configuration.json
548 Bytes
| { | |
| "seed": 42, | |
| "model_source": "FacebookAI/xlm-roberta-base", | |
| "model_revision": "e73636d4f797dec63c3081bb6ed5c7b0bb3f2089", | |
| "max_length": 512, | |
| "expected_world_size": 2, | |
| "per_device_train_batch_size": 4, | |
| "gradient_accumulation_steps": 4, | |
| "epochs": 5, | |
| "learning_rate": 2e-05, | |
| "weight_decay": 0.01, | |
| "warmup_fraction": 0.1, | |
| "optimizer": "adamw_torch", | |
| "fp16": true, | |
| "bf16": false, | |
| "precision": "float16_mixed", | |
| "training_method": "full_finetune", | |
| "resolved_model_revision": "e73636d4f797dec63c3081bb6ed5c7b0bb3f2089" | |
| } | |