Token Classification
Transformers
PyTorch
Safetensors
Faroese
xlm-roberta
part-of-speech
Eval Results (legacy)
Instructions to use wietsedv/xlm-roberta-base-ft-udpos28-fo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wietsedv/xlm-roberta-base-ft-udpos28-fo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="wietsedv/xlm-roberta-base-ft-udpos28-fo")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-fo") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-fo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from wietsedv/xlm-roberta-base-ft-udpos28-fo: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/wietsedv/xlm-roberta-base-ft-udpos28-fo/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://wietsedv/xlm-roberta-base-ft-udpos28-fo/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/wietsedv/xlm-roberta-base-ft-udpos28-fo/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- 4467b481b8a8f85aacfaa59fe4792f0abcb5e3504a00b12198e1de8decc07bf8
- Size of remote file:
- 1.11 GB
- SHA256:
- 817e98d8525b2671adc35f73c6ebc102076fd5614790e1fb214d31f78274ed54
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.