model documentation

README.md

@@ -1,15 +1,174 @@
+
 ---
-language: "en"
+language: en
+license: apache-2.0
 datasets:
 - squad
 metrics:
 - squad
-license: apache-2.0
 ---
 
-# ONNX Conversion of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-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>
 
-# DistilBERT base cased distilled SQuAD
+```python
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+ 
+tokenizer = AutoTokenizer.from_pretrained("philschmid/distilbert-onnx")
+ 
+model = AutoModelForQuestionAnswering.from_pretrained("philschmid/distilbert-onnx")
+ 
+ ```
+</details>
 
-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).