Instructions to use MoritzLaurer/bge-m3-zeroshot-v2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MoritzLaurer/bge-m3-zeroshot-v2.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="MoritzLaurer/bge-m3-zeroshot-v2.0")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MoritzLaurer/bge-m3-zeroshot-v2.0") model = AutoModelForSequenceClassification.from_pretrained("MoritzLaurer/bge-m3-zeroshot-v2.0", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Few-shot in this model
#7
by LGFU - opened
In the scorecard, there is a performance increase for few-shot prompts. Do I integrate the few-shot prompts into the hypothesis?
Thank you
in this case, few-shot means that the model has seen some example texts for the same task during training. There is nothing you need to change during inference. It's not like with a generative LLM, where you need to add few-shot examples in a prompt. The model will have this performance on the respective datasets by default, because it has seen some training data from the task.
Got it, thank you!