| |
|
| | --- |
| | 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> |
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