---
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The ALBERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English] Wikipedia(https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers).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>