---
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](https://huggingface.co/distilbert-base-cased-distilled-squad)
  - **Parent Model:** distilbert
- **Resources for more information:**
     - [Space](https://huggingface.co/spaces/krrishD/philschmid_distilbert-onnx)
     - [Blog Post](https://www.philschmid.de/convert-transformers-to-onnx)
 
# 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)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). 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](https://huggingface.co/datasets/squad) for further details.
 
## Training Procedure
 
 
### Preprocessing
 
See the [distilbert-base-cased model card](https://huggingface.co/distilbert-base-cased) for further details.
 
### Speeds, Sizes, Times
 
 
See the [distilbert-base-cased model card](https://huggingface.co/distilbert-base-cased) 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](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
- **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]
 
1. 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.
 
<details>
<summary> Click to expand </summary>

```python
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
 
tokenizer = AutoTokenizer.from_pretrained("philschmid/distilbert-onnx")
 
model = AutoModelForQuestionAnswering.from_pretrained("philschmid/distilbert-onnx")
 
 ```
</details>