Token Classification
Transformers
PyTorch
Safetensors
Bulgarian
xlm-roberta
part-of-speech
Eval Results (legacy)
Instructions to use wietsedv/xlm-roberta-base-ft-udpos28-bg 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-bg 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-bg")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-bg") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-bg") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 10e48b6487cf778d2eaf7bf96d0884feb77e5e5ea8d6900f61c03cf96e079e5b
- Size of remote file:
- 1.11 GB
- SHA256:
- a9a3d68c6a792daa20e899008f840ab900e9ec4cd2b9d684dc7485e2618ed847
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