Instructions to use VCNC/bert_piezas3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VCNC/bert_piezas3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VCNC/bert_piezas3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VCNC/bert_piezas3") model = AutoModelForSequenceClassification.from_pretrained("VCNC/bert_piezas3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- db6e2e42c3b26b7fa0a3501bd4799184d683f8fe266522232d5d0b737370497c
- Size of remote file:
- 692 MB
- SHA256:
- ca4fb7cac60373df644875b409cf7b8b1a54294fd2d7c5be49dcb39f82780b53
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.