| --- |
| tags: |
| - bert |
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| --- |
| # Model Card for bert-small-mm_retrieval-table_encoder |
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| # Model Details |
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| ## Model Description |
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| - **Developed by:** deepset |
| - **Shared by [Optional]:** More information needed |
| - **Model type:** More information needed |
| - **Language(s) (NLP):** More information needed |
| - **License:** More information needed |
| - **Related Models:** |
| - **Parent Model:** More information needed |
| - **Resources for more information:** |
| - [Associated Paper](https://arxiv.org/abs/1908.08962) |
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| # Uses |
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| ## Direct Use |
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| More information needed |
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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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| More information needed |
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| ## Training Procedure |
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| ### Preprocessing |
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| More information needed |
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| ### Speeds, Sizes, Times |
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| More information needed |
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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 |
| ## Results |
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| More information needed |
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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 |
| - **Hours used:** More information needed |
| - **Cloud Provider:** More information needed |
| - **Compute Region:** More information needed |
| - **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 |
| More information needed |
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| # Citation |
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| **BibTeX:** |
| ``` |
| @misc{bhargava2021generalization, |
| title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics}, |
| author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers}, |
| year={2021}, |
| eprint={2110.01518}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL} |
| } |
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| @article{DBLP:journals/corr/abs-1908-08962, |
| author = {Iulia Turc and |
| Ming{-}Wei Chang and |
| Kenton Lee and |
| Kristina Toutanova}, |
| title = {Well-Read Students Learn Better: The Impact of Student Initialization |
| on Knowledge Distillation}, |
| journal = {CoRR}, |
| volume = {abs/1908.08962}, |
| year = {2019}, |
| url = {http://arxiv.org/abs/1908.08962}, |
| eprinttype = {arXiv}, |
| eprint = {1908.08962}, |
| timestamp = {Thu, 29 Aug 2019 16:32:34 +0200}, |
| biburl = {https://dblp.org/rec/journals/corr/abs-1908-08962.bib}, |
| bibsource = {dblp computer science bibliography, https://dblp.org} |
| } |
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| ``` |
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| # Glossary [optional] |
| More information needed |
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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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| Deepset 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> |
| <summary> Click to expand </summary> |
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| ```python |
| from transformers import AutoTokenizer, DPRContextEncoder |
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| tokenizer = AutoTokenizer.from_pretrained("deepset/bert-small-mm_retrieval-table_encoder") |
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| model = DPRContextEncoder.from_pretrained("deepset/bert-small-mm_retrieval-table_encoder") |
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| ``` |
| </details> |
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