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--- |
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language: en |
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tags: |
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- text-classification |
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- albert |
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--- |
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# Model Card for albert-base-rci-wikisql-col |
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# Model Details |
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## Model Description |
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More information needed |
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- **Developed by:** Michael Glass |
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- **Shared by [Optional]:** Michael Glass |
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- **Model type:** Token Classification |
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- **Language(s) (NLP):** English |
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- **License:** More information needed |
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- **Parent Model:** [ALBERT Base v2](https://huggingface.co/albert-base-v2?text=The+goal+of+life+is+%5BMASK%5D.) |
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- **Resources for more information:** |
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- [ALBERT Base GitHub Repo](https://github.com/jhyuklee/biobert) |
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- [ALBERT Base Paper](https://github.com/google-research/albert) |
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# Uses |
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## Direct Use |
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This model can be used for the task of text classification. |
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> This model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. |
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See [ALBERT Base v2 model card](https://huggingface.co/albert-base-v2?text=The+goal+of+life+is+%5BMASK%5D.) for more information. |
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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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For tasks such as text generation you should look at model like GPT2. |
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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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The ALBERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English] Wikipedia(https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). |
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See [ALBERT Base v2 model card](https://huggingface.co/albert-base-v2?text=The+goal+of+life+is+%5BMASK%5D.) for more information. |
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## Training Procedure |
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### Preprocessing |
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>The texts are lowercased and tokenized using SentencePiece and a vocabulary size of 30,000. The inputs of the model are |
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then of the form: |
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``` |
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[CLS] Sentence A [SEP] Sentence B [SEP] |
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``` |
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See [ALBERT Base v2 model card](https://huggingface.co/albert-base-v2?text=The+goal+of+life+is+%5BMASK%5D.) for more information. |
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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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More information needed |
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### Metrics |
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More information needed |
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## 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 |
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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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```bibtex |
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@article{DBLP:journals/corr/abs-1909-11942, |
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author = {Zhenzhong Lan and |
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Mingda Chen and |
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Sebastian Goodman and |
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Kevin Gimpel and |
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Piyush Sharma and |
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Radu Soricut}, |
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title = {{ALBERT:} {A} Lite {BERT} for Self-supervised Learning of Language |
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Representations}, |
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journal = {CoRR}, |
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volume = {abs/1909.11942}, |
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year = {2019}, |
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url = {http://arxiv.org/abs/1909.11942}, |
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archivePrefix = {arXiv}, |
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eprint = {1909.11942}, |
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timestamp = {Fri, 27 Sep 2019 13:04:21 +0200}, |
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biburl = {https://dblp.org/rec/journals/corr/abs-1909-11942.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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``` |
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**APA:** |
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More information needed |
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# Glossary [optional] |
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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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Michael Glass 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, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("michaelrglass/albert-base-rci-wikisql-col") |
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model = AutoModelForSequenceClassification.from_pretrained("michaelrglass/albert-base-rci-wikisql-col") |
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``` |
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</details> |
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