Text Classification
Transformers
PyTorch
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use franfj/DIPROMATS_subtask_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use franfj/DIPROMATS_subtask_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="franfj/DIPROMATS_subtask_1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("franfj/DIPROMATS_subtask_1") model = AutoModelForSequenceClassification.from_pretrained("franfj/DIPROMATS_subtask_1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 225803b62ef6d856bdc1f3fcdfc52d99b28d5273bc4adc71c0cfa5a790c4d682
- Size of remote file:
- 1.11 GB
- SHA256:
- 6ad5716b0db8c439f5b9a26fc30a1241ec8c9e998384afc47cf96fa8ca417c84
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.