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/release_verification.json from mehedi4/SafeAge-XLM-R-Base: direct link, hf CLI and curl.
- Browser
- Download file 2.52 kB
-
https://huggingface.co/mehedi4/SafeAge-XLM-R-Base/resolve/main/provenance/release_verification.json
- Command line
-
hf download hf://mehedi4/SafeAge-XLM-R-Base/provenance/release_verification.json
-
curl -L -o release_verification.json https://huggingface.co/mehedi4/SafeAge-XLM-R-Base/resolve/main/provenance/release_verification.json
2.52 kB
| { | |
| "repository": "mehedi4/SafeAge-XLM-R-Base", | |
| "original_checkpoint_directory": "xlmr/safeage_v3_xlmr_base_full_seed42_20261024_144752_7006aa/best_model", | |
| "all_original_checkpoint_files_verified": true, | |
| "original_file_count": 7, | |
| "original_checkpoint_sha256": { | |
| "config.json": "9438e0e2e31d4e3d23c2dc7c03d828c2fe935ece253961bfc8a1d83dbba59aea", | |
| "decision_threshold.json": "5e205df03ac49585e4f32fd21bf2947ecd11470bd380ab2af21dca8c08638f93", | |
| "model.safetensors": "050b292486894af1e841a0d4a9f6a3992f5bb3413a4ddeb59806d6a458dce442", | |
| "tokenizer.json": "acbd420e2269cdc1ef45332d3d5c418be4aef6b8cb5a0b7ccae0893485307153", | |
| "tokenizer_config.json": "159697d23b04b1e7a607e737f5d331e2d356e8484080d723a9495dafc58caab1", | |
| "trainer_state.json": "9234ad8b94308a82c0a487c5265167d8f5afea7cde4174e006fb68a2475cf431", | |
| "training_args.bin": "dcc4257d8b83dec675a33e0ee1af4429f34c4f1bc5a015012563a466d0c5eb20" | |
| }, | |
| "published_original_files": [ | |
| "config.json", | |
| "decision_threshold.json", | |
| "model.safetensors", | |
| "tokenizer.json", | |
| "tokenizer_config.json" | |
| ], | |
| "excluded_training_only_files": [ | |
| "trainer_state.json", | |
| "training_args.bin" | |
| ], | |
| "all_published_checkpoint_files_unchanged": true, | |
| "model_tensor_count": 201, | |
| "model_parameter_count": 278045186, | |
| "classifier_tensors": { | |
| "classifier.dense.bias": { | |
| "dtype": "F32", | |
| "shape": [ | |
| 768 | |
| ], | |
| "data_offsets": [ | |
| 0, | |
| 3072 | |
| ] | |
| }, | |
| "classifier.dense.weight": { | |
| "dtype": "F32", | |
| "shape": [ | |
| 768, | |
| 768 | |
| ], | |
| "data_offsets": [ | |
| 3072, | |
| 2362368 | |
| ] | |
| }, | |
| "classifier.out_proj.bias": { | |
| "dtype": "F32", | |
| "shape": [ | |
| 2 | |
| ], | |
| "data_offsets": [ | |
| 2362368, | |
| 2362376 | |
| ] | |
| }, | |
| "classifier.out_proj.weight": { | |
| "dtype": "F32", | |
| "shape": [ | |
| 2, | |
| 768 | |
| ], | |
| "data_offsets": [ | |
| 2362376, | |
| 2368520 | |
| ] | |
| } | |
| }, | |
| "base_model_revision": "e73636d4f797dec63c3081bb6ed5c7b0bb3f2089", | |
| "threshold": 0.99, | |
| "inference_script_syntax_valid": true, | |
| "release_new_inference_performed": false, | |
| "original_model_reload_check": { | |
| "status": "PASS", | |
| "validation_rows": 8, | |
| "maximum_probability_difference": 4.270144715601276e-08 | |
| }, | |
| "license_selection_source": "Pinned upstream XLM-R card declares MIT; upstream fairseq copyright preserved; MIT for SafeAge modifications follows the authors' existing SafeAge Guard release." | |
| } | |