Instructions to use deepset/xlm-roberta-base-squad2-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/xlm-roberta-base-squad2-distilled with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="deepset/xlm-roberta-base-squad2-distilled")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("deepset/xlm-roberta-base-squad2-distilled") model = AutoModelForQuestionAnswering.from_pretrained("deepset/xlm-roberta-base-squad2-distilled", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
TemporalMesh Transformer: 29.4 PPL at 48% compute — beats Mamba, new open-source architecture
#7 opened 5 months ago
by
vigneshwar234
[AUTOMATED] Model Memory Requirements
#6 opened about 3 years ago
by
model-sizer-bot
This is roberta-base not distilled
4
#4 opened about 4 years ago
by
bilalghanem
Add evaluation results on the squad_v2 config of squad_v2
#3 opened about 4 years ago
by
autoevaluator
Add evaluation results on the squad_v2 config of squad_v2
7
#1 opened over 4 years ago
by
autoevaluator