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
Resources for more information:
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 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) and Bender et al. (2021)). 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The ALBERT model was pretrained on BookCorpus, a dataset consisting of 11,038 unpublished books and [English] Wikipedia(https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers).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 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 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 presented in Lacoste et al. (2019).
- 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
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Hardware
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Software
More information needed.
Citation
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:
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Glossary [optional]
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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.
Click to expand
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")