Instructions to use eryawww/xlmr_base_nlit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eryawww/xlmr_base_nlit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eryawww/xlmr_base_nlit")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eryawww/xlmr_base_nlit") model = AutoModelForSequenceClassification.from_pretrained("eryawww/xlmr_base_nlit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from eryawww/xlmr_base_nlit: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/eryawww/xlmr_base_nlit/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://eryawww/xlmr_base_nlit/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/eryawww/xlmr_base_nlit/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- 703afc78055547bd2918a89794265866947b5692800b8b1e8bccd471e5af3bc1
- Size of remote file:
- 1.11 GB
- SHA256:
- 950b58e47ae18a3e3332a2800774cd9f5cf079528af4dbca7134bf6833c0ed30
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