Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use fredymad/siebert_laxo_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_laxo_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_laxo_2e-5_16_2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fredymad/siebert_laxo_2e-5_16_2") model = AutoModelForSequenceClassification.from_pretrained("fredymad/siebert_laxo_2e-5_16_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- db5a782c9ec3b54de098dc3d4eb256163a47556fac671dd3f1cc8cd9bc84513b
- Size of remote file:
- 1.42 GB
- SHA256:
- f6e75c76eb8c0a5a5ce239367bf7bba06ca4661d06413add73d1fe8266787241
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.