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--- |
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language: en |
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license: apache-2.0 |
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datasets: |
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- squad |
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metrics: |
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- squad |
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--- |
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# Model Card for ONNX Conversion of distilbert-base-cased-distilled-squad |
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# Model Details |
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## Model Description |
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This model is a fine-tune checkpoint of DistilBERT-base-cased, fine-tuned using (a second step of) knowledge distillation on SQuAD v1.1. |
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- **Developed by:** Philipp Schmid |
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- **Shared by [Optional]:** Hugging Face |
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- **Model type:** Question Answering |
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- **Language(s) (NLP):** en |
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- **License:** Apache-2.0 |
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- **Related Models:** [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad) |
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- **Parent Model:** distilbert |
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- **Resources for more information:** |
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- [Space](https://huggingface.co/spaces/krrishD/philschmid_distilbert-onnx) |
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- [Blog Post](https://www.philschmid.de/convert-transformers-to-onnx) |
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# Uses |
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## Direct Use |
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This model can be used for question answering. |
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## Downstream Use [Optional] |
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More information needed. |
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## Out-of-Scope Use |
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The model should not be used to intentionally create hostile or alienating environments for people. |
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# Bias, Risks, and Limitations |
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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. |
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## Recommendations |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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# Training Details |
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## Training Data |
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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. |
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## Training Procedure |
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### Preprocessing |
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See the [distilbert-base-cased model card](https://huggingface.co/distilbert-base-cased) for further details. |
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### Speeds, Sizes, Times |
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See the [distilbert-base-cased model card](https://huggingface.co/distilbert-base-cased) for further details. |
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# Evaluation |
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## Testing Data, Factors & Metrics |
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### Testing Data |
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More information needed |
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### Factors |
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### Metrics |
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More information needed |
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## Results |
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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). |
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# Model Examination |
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More information needed |
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# Environmental Impact |
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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). |
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- **Hardware Type:** More information needed |
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- **Hours used:** More information needed |
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- **Cloud Provider:** More information needed |
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- **Compute Region:** More information needed |
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- **Carbon Emitted:** More information needed |
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# Technical Specifications [optional] |
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## Model Architecture and Objective |
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More information needed |
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## Compute Infrastructure |
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More information needed |
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### Hardware |
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More information needed |
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### Software |
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More information needed |
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# Citation |
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**BibTeX:** |
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More information needed |
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**APA:** |
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More information needed |
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# Glossary [optional] |
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1. What is ONNX? |
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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. |
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# More Information [optional] |
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More information needed |
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# Model Card Authors [optional] |
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Philipp Schmid in collaboration with Ezi Ozoani and the Hugging Face team. |
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# Model Card Contact |
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More information needed |
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# How to Get Started with the Model |
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Use the code below to get started with the model. |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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from transformers import AutoTokenizer, AutoModelForQuestionAnswering |
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tokenizer = AutoTokenizer.from_pretrained("philschmid/distilbert-onnx") |
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model = AutoModelForQuestionAnswering.from_pretrained("philschmid/distilbert-onnx") |
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``` |
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</details> |
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