Instructions to use muhtasham/small-mlm-glue-cola with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use muhtasham/small-mlm-glue-cola with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="muhtasham/small-mlm-glue-cola")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("muhtasham/small-mlm-glue-cola") model = AutoModelForMaskedLM.from_pretrained("muhtasham/small-mlm-glue-cola") - Notebooks
- Google Colab
- Kaggle
small-mlm-glue-cola
This model is a fine-tuned version of google/bert_uncased_L-4_H-512_A-8 on the None dataset. It achieves the following results on the evaluation set:
- Loss: nan
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.0589 | 0.47 | 500 | 2.8255 |
| 2.8708 | 0.94 | 1000 | 2.8047 |
| 2.7086 | 1.4 | 1500 | 2.6590 |
| 2.6021 | 1.87 | 2000 | 2.7510 |
| 2.4549 | 2.34 | 2500 | 2.8776 |
| 2.4864 | 2.81 | 3000 | nan |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
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