Instructions to use hsila/chembed-plug-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hsila/chembed-plug-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hsila/chembed-plug-2", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hsila/chembed-plug-2", trust_remote_code=True) model = AutoModel.from_pretrained("hsila/chembed-plug-2", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
File size: 270 Bytes
1d7d644 | 1 2 3 4 5 6 7 8 9 | {
"word_embedding_dimension": 768,
"pooling_mode_cls_token": false,
"pooling_mode_mean_tokens": true,
"pooling_mode_max_tokens": false,
"pooling_mode_mean_sqrt_len_tokens": false,
"pooling_mode_weightedmean_tokens": false,
"pooling_mode_lasttoken": false
} |