Model Card for WeightWatcher/albert-large-v2-sst2
This model was finetuned on the GLUE/sst2 task, based on the pretrained
albert-large-v2 model. Hyperparameters were (largely) taken from the following
publication, with some minor exceptions.
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://arxiv.org/abs/1909.11942Model Details
Model Description
- Developed by: https://huggingface.co/cdhinrichs
- Model type: Text Sequence Classification
- Language(s) (NLP): English
- License: MIT
- Finetuned from model: https://huggingface.co/albert-large-v2
Uses
Text classification, research and development.
Out-of-Scope Use
Bias, Risks, and Limitations
Recommendations
How to Get Started with the Model
Use the code below to get started with the model.
1from transformers import AlbertForSequenceClassification
2model = AlbertForSequenceClassification.from_pretrained("WeightWatcher/albert-large-v2-sst2")
Training Details
Training Data
SST2 is a classification task, and a part of the GLUE benchmark.
Training Procedure
Adam optimization was used on the pretrained ALBERT model at
https://huggingface.co/albert-large-v2.
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://arxiv.org/abs/1909.11942Training Hyperparameters
Training hyperparameters, (Learning Rate, Batch Size, ALBERT dropout rate,
Classifier Dropout Rate, Warmup Steps, Training Steps,) were taken from Table
A.4 in,
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://arxiv.org/abs/1909.11942Max sequence length (MSL) was set to 128, differing from the above.
Evaluation
Classification accuracy is used to evaluate model performance.
Testing Data, Factors & Metrics
Testing Data
Metrics
Classification accuracy
Results
Training Classification accuracy: 0.9990794221146565
Evaluation Classification accuracy: 0.9461009174311926
Environmental Impact
The model was finetuned on a single user workstation with a single GPU. CO2
impact is expected to be minimal.
