model documentation

README.md

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+---
+tags:
+- bert
+
+---
+# Model Card for bert-small-mm_retrieval-table_encoder
+ 
+ 
+# Model Details
+ 
+## Model Description
+ 
+- **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)
+ 
+ 
+# Uses
+ 
+ 
+## Direct Use
+ 
+More information needed
+ 
+## 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
+ 
+More information needed
+ 
+## Training Procedure
+ 
+ 
+### Preprocessing
+ 
+More information needed
+ 
+### Speeds, Sizes, Times
+ 
+More information needed
+ 
+# Evaluation
+ 
+ 
+## Testing Data, Factors & Metrics
+ 
+### Testing Data
+ 
+More information needed
+ 
+### Factors
+ 
+ 
+### Metrics
+ 
+More information needed
+## Results 
+ 
+More information needed
+ 
+# 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:**
+ ```
+@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}
+}
+ 
+@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}
+}
+ 
+ 
+```
+ 
+ 
+# Glossary [optional]
+More information needed
+ 
+# More Information [optional]
+ 
+More information needed
+ 
+# Model Card Authors [optional]
+ 
+ 
+Deepset 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, DPRContextEncoder
+ 
+tokenizer = AutoTokenizer.from_pretrained("deepset/bert-small-mm_retrieval-table_encoder")
+ 
+model = DPRContextEncoder.from_pretrained("deepset/bert-small-mm_retrieval-table_encoder")
+ 
+```
+</details>