Feature Extraction
Transformers
PyTorch
Italian
bert
BERT
encoder
embeddings
TiME
size:m
text-embeddings-inference
Instructions to use dschulmeist/TiME-it-m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dschulmeist/TiME-it-m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dschulmeist/TiME-it-m")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dschulmeist/TiME-it-m") model = AutoModel.from_pretrained("dschulmeist/TiME-it-m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 855deac8220907e53c308d4ebb9552311c4a607f6fa2bc543c345bdc0a0a120f
- Size of remote file:
- 942 MB
- SHA256:
- a368e8de3598d458c7a339890224dd9b1bd076b42aa39cb9c977b37578eb7034
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