Instructions to use philschmid/distilbert-neuron with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use philschmid/distilbert-neuron with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="philschmid/distilbert-neuron")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("philschmid/distilbert-neuron") model = AutoModelForQuestionAnswering.from_pretrained("philschmid/distilbert-neuron", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
language: en
datasets:
- squad
metrics:
- squad
license: apache-2.0
AWS Neuron Conversion of distilbert-base-cased-distilled-squad
DistilBERT base cased distilled SQuAD
This model is a fine-tune checkpoint of DistilBERT-base-cased, fine-tuned using (a second step of) knowledge distillation on SQuAD v1.1. This model reaches a F1 score of 87.1 on the dev set (for comparison, BERT bert-base-cased version reaches a F1 score of 88.7).