Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use fredymad/siebert_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/siebert_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/siebert_estricto_2e-5_16_2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fredymad/siebert_estricto_2e-5_16_2") model = AutoModelForSequenceClassification.from_pretrained("fredymad/siebert_estricto_2e-5_16_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 860ee7315c86cafa8afdb5066e55a85ef3c7ccff1d3130020bd61e1c43574b24
- Size of remote file:
- 1.42 GB
- SHA256:
- ff7570d30826644754af72e3d94681e5371d917077a529aae86df5abdc4ed4e4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.