language: en
license: apache-2.0
datasets:
- squad
metrics:
- squad
Model Card for ONNX Conversion of distilbert-base-cased-distilled-squad
Model Details
Model Description
This model is a fine-tune checkpoint of DistilBERT-base-cased, fine-tuned using (a second step of) knowledge distillation on SQuAD v1.1.
- Developed by: Philipp Schmid
- Shared by [Optional]: Hugging Face
- Model type: Question Answering
- Language(s) (NLP): en
- License: Apache-2.0
- Related Models: distilbert-base-cased-distilled-squad
- Parent Model: distilbert
- Resources for more information:
Uses
Direct Use
This model can be used for question answering.
Downstream Use [Optional]
More information needed.
Out-of-Scope Use
The model should not be used to intentionally create hostile or alienating environments for people.
Bias, Risks, and Limitations
Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
Training Details
Training Data
To learn more about the SQuAD v1.1 dataset, see the associated SQuAD v1.1 dataset card for further details.
Training Procedure
Preprocessing
See the distilbert-base-cased model card for further details.
Speeds, Sizes, Times
See the distilbert-base-cased model card for further details.
Evaluation
Testing Data, Factors & Metrics
Testing Data
More information needed
Factors
Metrics
More information needed
Results
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).
Model Examination
More information needed
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: More information needed
- Hours used: More information needed
- Cloud Provider: More information needed
- Compute Region: More information needed
- Carbon Emitted: More information needed
Technical Specifications [optional]
Model Architecture and Objective
More information needed
Compute Infrastructure
More information needed
Hardware
More information needed
Software
More information needed
Citation
BibTeX:
More information needed
APA:
More information needed
Glossary [optional]
- What is ONNX? The ONNX (Open Neural Network eXchange) is an open standard and format to represent machine learning models. ONNX defines a common set of operators and a common file format to represent deep learning models in a wide variety of frameworks, including PyTorch and TensorFlow.
More Information [optional]
More information needed
Model Card Authors [optional]
Philipp Schmid in collaboration with Ezi Ozoani and the Hugging Face team.
Model Card Contact
More information needed
How to Get Started with the Model
Use the code below to get started with the model.
Click to expand
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("philschmid/distilbert-onnx")
model = AutoModelForQuestionAnswering.from_pretrained("philschmid/distilbert-onnx")