model documentation

README.md

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+The ALBERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English] Wikipedia(https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers).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