Instructions to use T-Systems-onsite/cross-en-zh-roberta-sentence-transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use T-Systems-onsite/cross-en-zh-roberta-sentence-transformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="T-Systems-onsite/cross-en-zh-roberta-sentence-transformer")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("T-Systems-onsite/cross-en-zh-roberta-sentence-transformer") model = AutoModel.from_pretrained("T-Systems-onsite/cross-en-zh-roberta-sentence-transformer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": "<mask>", "model_max_length": 512, "special_tokens_map_file": "input_models/johngiorgi-declutr-small-all-paraphrase-multilingual/0_Transformer/special_tokens_map.json", "full_tokenizer_file": null, "tokenizer_file": null, "name_or_path": "/home/phmay/.cache/torch/sentence_transformers/sbert.net_models_xlm-r-distilroberta-base-paraphrase-v1/0_Transformer"} |