Instructions to use Cheatham/xlm-roberta-large-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cheatham/xlm-roberta-large-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cheatham/xlm-roberta-large-finetuned")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cheatham/xlm-roberta-large-finetuned") model = AutoModelForSequenceClassification.from_pretrained("Cheatham/xlm-roberta-large-finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from Cheatham/xlm-roberta-large-finetuned: direct link, hf CLI and curl.
- Browser
- Download file 611 Bytes
-
https://huggingface.co/Cheatham/xlm-roberta-large-finetuned/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://Cheatham/xlm-roberta-large-finetuned/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Cheatham/xlm-roberta-large-finetuned/resolve/main/tokenizer_config.json
611 Bytes
| {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "sp_model_kwargs": {}, "model_max_length": 512, "special_tokens_map_file": null, "tokenizer_file": "/root/.cache/huggingface/transformers/7766c86e10505ed9b39af34e456480399bf06e35b36b8f2b917460a2dbe94e59.a984cf52fc87644bd4a2165f1e07e0ac880272c1e82d648b4674907056912bd7", "name_or_path": "xlm-roberta-large", "tokenizer_class": "XLMRobertaTokenizer"} |