Token Classification
Transformers
PyTorch
Safetensors
Greek
xlm-roberta
part-of-speech
Eval Results (legacy)
Instructions to use wietsedv/xlm-roberta-base-ft-udpos28-el 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-el 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-el")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-el") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-el", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from wietsedv/xlm-roberta-base-ft-udpos28-el: direct link, hf CLI and curl.
- Browser
- Download file 454 Bytes
-
https://huggingface.co/wietsedv/xlm-roberta-base-ft-udpos28-el/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://wietsedv/xlm-roberta-base-ft-udpos28-el/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/wietsedv/xlm-roberta-base-ft-udpos28-el/resolve/main/tokenizer_config.json
454 Bytes
| {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "add_prefix_space": true, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "output/xlm-roberta-base_ft_udpos28-el/1d6ca3e8", "tokenizer_class": "XLMRobertaTokenizer"} |