Feature Extraction
Transformers
Safetensors
English
roberta
pharmacore
sparse
drug-discovery
apple-silicon
chemberta
molecular-language-model
cheminformatics
smiles
pruning
efficient-inference
Eval Results (legacy)
text-embeddings-inference
Instructions to use stephenjun8192/chemberta-zinc-sparse50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stephenjun8192/chemberta-zinc-sparse50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="stephenjun8192/chemberta-zinc-sparse50")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("stephenjun8192/chemberta-zinc-sparse50") model = AutoModel.from_pretrained("stephenjun8192/chemberta-zinc-sparse50") - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_prefix_space": false, | |
| "backend": "tokenizers", | |
| "bos_token": "<s>", | |
| "cls_token": "<s>", | |
| "eos_token": "</s>", | |
| "errors": "replace", | |
| "is_local": false, | |
| "mask_token": "<mask>", | |
| "max_len": 512, | |
| "model_max_length": 512, | |
| "pad_token": "<pad>", | |
| "sep_token": "</s>", | |
| "tokenizer_class": "RobertaTokenizer", | |
| "trim_offsets": true, | |
| "unk_token": "<unk>" | |
| } | |