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
| language: "en" | |
| datasets: | |
| - squad | |
| metrics: | |
| - squad | |
| license: apache-2.0 | |
| # AWS Neuron Conversion of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad) | |
| # DistilBERT base cased distilled SQuAD | |
| This model is a fine-tune checkpoint of [DistilBERT-base-cased](https://huggingface.co/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). | |