Token Classification
Transformers
PyTorch
Safetensors
Hebrew
xlm-roberta
part-of-speech
Eval Results (legacy)
Instructions to use wietsedv/xlm-roberta-base-ft-udpos28-he 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-he 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-he")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-he") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-he", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from wietsedv/xlm-roberta-base-ft-udpos28-he: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/wietsedv/xlm-roberta-base-ft-udpos28-he/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://wietsedv/xlm-roberta-base-ft-udpos28-he/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/wietsedv/xlm-roberta-base-ft-udpos28-he/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- 5b160e1f97872d3debfe139762571432875302281a7fc5606255348b710c5581
- Size of remote file:
- 1.11 GB
- SHA256:
- 909f1adfae654ec88d08bfea469a3ae3ec6d777daa5026728aea7e3fb9a9e24d
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