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 SHA256SUMS from mehedi4/SafeAge-XLM-R-Base: direct link, hf CLI and curl.
- Browser
- Download file 1.94 kB
-
https://huggingface.co/mehedi4/SafeAge-XLM-R-Base/resolve/main/SHA256SUMS
- Command line
-
hf download hf://mehedi4/SafeAge-XLM-R-Base/SHA256SUMS
-
curl -L -o SHA256SUMS https://huggingface.co/mehedi4/SafeAge-XLM-R-Base/resolve/main/SHA256SUMS
1.94 kB
| fb5a8d4e1db3b510fdec89f13a0bd7b04e2340dd3fa090e19618b33f0b8eacd1 LICENSE | |
| 52412d7bc7ce4157ea628bbaacb8829e0a9cb3c58f57f99176126bc8cf2bfc85 LICENSE.base-model.mit | |
| 155fef939c4b3457a7674a19c8c78a10a15731e159b074b1f3b45fd5b30ca8d3 README.md | |
| 886e8ca7cd4f2275bd9ef27ef300947720772e890cb5759b4102f6cd5f97c709 base_model_reference.json | |
| 9438e0e2e31d4e3d23c2dc7c03d828c2fe935ece253961bfc8a1d83dbba59aea config.json | |
| 5e205df03ac49585e4f32fd21bf2947ecd11470bd380ab2af21dca8c08638f93 decision_threshold.json | |
| 5ad15a1e7c8bfd423f2b9aed05a4439dfffb6f1eb564a8f769f49896fe6f2b4b evaluation/test_by_language_variant.csv | |
| f1c4650dc4c37a88c61b18dc3a6217737133ca80f98103de57898ff43497cc8c evaluation/test_metrics.json | |
| 53325b09949b6cc71e6ffba4d22fc942e3c57f1c68670004367c06545e31a543 inference.py | |
| 050b292486894af1e841a0d4a9f6a3992f5bb3413a4ddeb59806d6a458dce442 model.safetensors | |
| 9ba46d0d822b7a3b52f37a6e598285f78f493e3a898473a2481c02683c5e37b1 provenance/environment.json | |
| 4a0b373298c9cb9b3cbb49a2bc96378cb4ea8aabf7e7afbffa459a1e15426d28 provenance/model_reload_check.json | |
| 17d1b70eb1aa582d07372c1993db738bf0a52fd1a04bc6d791553a29ec90ef73 provenance/original_checkpoint_sha256.json | |
| 840bacdd195a9c6c825fabf9cafa8efd032a7e8073ca181d4c7546a2102cdd08 provenance/release_verification.json | |
| b378ed2a1680acf4f113e81df1094b4ec9ee76625402b5c3b9e77e6f58b59525 provenance/training_configuration.json | |
| bc126adec626ce03e965030c2638d3e3358565614e0189268f81cd2023d0bc5f provenance/upstream_license_source.json | |
| bec9f2cd81f7396a64ee474145117d81139906443a21480cbe4993c97cb499aa provenance/upstream_model_card.md | |
| 006e42d58819f1bc1f876431ac60579ce182f586a17b0be69b60d1bdeaf1cb46 provenance/weight_update_check.json | |
| c7e44a25ec692b836b4336d66c185fa9c0456109dd64b88cd50ccf8954e592c4 requirements.txt | |
| acbd420e2269cdc1ef45332d3d5c418be4aef6b8cb5a0b7ccae0893485307153 tokenizer.json | |
| 159697d23b04b1e7a607e737f5d331e2d356e8484080d723a9495dafc58caab1 tokenizer_config.json | |