Sentence Similarity
Transformers
PyTorch
English
deberta-v2
feature-extraction
PubChem
chemistry
biology
deberta-v3
text-embeddings-inference
Instructions to use mschuh/PubChemDeBERTa-augmented with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mschuh/PubChemDeBERTa-augmented with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("mschuh/PubChemDeBERTa-augmented") model = AutoModel.from_pretrained("mschuh/PubChemDeBERTa-augmented", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from mschuh/PubChemDeBERTa-augmented: direct link, hf CLI and curl.
- Browser
- Download file 412 Bytes
-
https://huggingface.co/mschuh/PubChemDeBERTa-augmented/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://mschuh/PubChemDeBERTa-augmented/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/mschuh/PubChemDeBERTa-augmented/resolve/main/tokenizer_config.json
412 Bytes
| { | |
| "bos_token": "[CLS]", | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "do_lower_case": false, | |
| "eos_token": "[SEP]", | |
| "mask_token": "[MASK]", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "sp_model_kwargs": {}, | |
| "split_by_punct": false, | |
| "tokenizer_class": "DebertaV2Tokenizer", | |
| "unk_token": "[UNK]", | |
| "vocab_type": "spm" | |
| } | |