Instructions to use mrm8488/spanbert-base-finetuned-tacred with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrm8488/spanbert-base-finetuned-tacred with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="mrm8488/spanbert-base-finetuned-tacred")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("mrm8488/spanbert-base-finetuned-tacred") model = AutoModel.from_pretrained("mrm8488/spanbert-base-finetuned-tacred") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7c99ecf86df2d5f7c0c86a10aaad41d022fe587bbb2f70a5439fa657a5595d25
- Size of remote file:
- 1.33 GB
- SHA256:
- 18f2710de5e6635c44800207e7acf1b91cfc454e87206d5d444ff037e189fa49
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.