Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use fredymad/bert_estricto_2e-5_16_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fredymad/bert_estricto_2e-5_16_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fredymad/bert_estricto_2e-5_16_2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fredymad/bert_estricto_2e-5_16_2") model = AutoModelForSequenceClassification.from_pretrained("fredymad/bert_estricto_2e-5_16_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a20a9634b20743ed691b982d3e3d0cee76685d1ed618ebe18ed5d4e46b150c6e
- Size of remote file:
- 438 MB
- SHA256:
- 116f5a6cb27e4b2583b1204363812fb3c76203a129a7b5a2e12cfa5ebc2c1425
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.