Token Classification
Transformers
PyTorch
Safetensors
Polish
xlm-roberta
part-of-speech
Eval Results (legacy)
Instructions to use wietsedv/xlm-roberta-base-ft-udpos28-pl 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-pl 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-pl")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-pl") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-pl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from wietsedv/xlm-roberta-base-ft-udpos28-pl: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/wietsedv/xlm-roberta-base-ft-udpos28-pl/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://wietsedv/xlm-roberta-base-ft-udpos28-pl/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/wietsedv/xlm-roberta-base-ft-udpos28-pl/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- 92b01be7618d2e0c7fc32a71b57e5709c93411f2080dddab4bea345ae2cd9658
- Size of remote file:
- 1.11 GB
- SHA256:
- 8a5b56528aea9d7c7eb5cd74388778b6db2d6d355e2c95aa348b050b24d2eabe
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