Feature Extraction
Transformers
PyTorch
multilingual
bert
STILT
retraining
multi-task learning
text-embeddings-inference
Instructions to use robvanderg/Sem-mmmBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use robvanderg/Sem-mmmBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="robvanderg/Sem-mmmBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("robvanderg/Sem-mmmBERT") model = AutoModel.from_pretrained("robvanderg/Sem-mmmBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from robvanderg/Sem-mmmBERT: direct link, hf CLI and curl.
- Browser
- Download file 1.96 MB
-
https://huggingface.co/robvanderg/Sem-mmmBERT/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://robvanderg/Sem-mmmBERT@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/robvanderg/Sem-mmmBERT/resolve/refs%2Fpr%2F1/tokenizer.json
1.96 MB
File too large to display, you can check the raw version instead.