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--- |
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language: |
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- "en" |
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license: mit |
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datasets: |
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- glue |
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metrics: |
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- Classification accuracy |
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--- |
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# Model Card for cdhinrichs/albert-large-v2-mnli |
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This model was finetuned on the GLUE/mnli task, based on the pretrained |
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albert-large-v2 model. Hyperparameters were (largely) taken from the following |
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publication, with some minor exceptions. |
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ALBERT: A Lite BERT for Self-supervised Learning of Language Representations |
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https://arxiv.org/abs/1909.11942 |
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## Model Details |
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### Model Description |
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- **Developed by:** https://huggingface.co/cdhinrichs |
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- **Model type:** Text Sequence Classification |
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- **Language(s) (NLP):** English |
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- **License:** MIT |
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- **Finetuned from model:** https://huggingface.co/albert-large-v2 |
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## Uses |
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Text classification, research and development. |
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### Out-of-Scope Use |
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Not intended for production use. |
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See https://huggingface.co/albert-large-v2 |
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## Bias, Risks, and Limitations |
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See https://huggingface.co/albert-large-v2 |
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### Recommendations |
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See https://huggingface.co/albert-large-v2 |
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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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```python |
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from transformers import AlbertForSequenceClassification |
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model = AlbertForSequenceClassification.from_pretrained("cdhinrichs/albert-large-v2-mnli") |
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``` |
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## Training Details |
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### Training Data |
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See https://huggingface.co/datasets/glue#mnli |
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MNLI is a classification task, and a part of the GLUE benchmark. |
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### Training Procedure |
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Adam optimization was used on the pretrained ALBERT model at |
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https://huggingface.co/albert-large-v2. |
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ALBERT: A Lite BERT for Self-supervised Learning of Language Representations |
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https://arxiv.org/abs/1909.11942 |
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#### Training Hyperparameters |
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Training hyperparameters, (Learning Rate, Batch Size, ALBERT dropout rate, |
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Classifier Dropout Rate, Warmup Steps, Training Steps,) were taken from Table |
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A.4 in, |
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ALBERT: A Lite BERT for Self-supervised Learning of Language Representations |
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https://arxiv.org/abs/1909.11942 |
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Max sequence length (MSL) was set to 128, differing from the above. |
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## Evaluation |
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Classification accuracy is used to evaluate model performance. |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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See https://huggingface.co/datasets/glue#mnli |
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#### Metrics |
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Classification accuracy |
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### Results |
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Training classification accuracy: 0.9567916639080015 |
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Evaluation classification accuracy: 0.86571574121243 |
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## Environmental Impact |
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The model was finetuned on a single user workstation with a single GPU. CO2 |
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impact is expected to be minimal. |
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