Instructions to use muhtasham/small-mlm-glue-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use muhtasham/small-mlm-glue-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="muhtasham/small-mlm-glue-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("muhtasham/small-mlm-glue-mnli") model = AutoModelForMaskedLM.from_pretrained("muhtasham/small-mlm-glue-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
small-mlm-glue-mnli
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: 2.8314
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 |
|---|---|---|---|
| 3.0194 | 0.4 | 500 | 2.7922 |
| 3.0037 | 0.8 | 1000 | 2.8022 |
| 2.9388 | 1.2 | 1500 | 2.7826 |
| 2.915 | 1.6 | 2000 | 2.7838 |
| 2.8626 | 2.0 | 2500 | 2.7769 |
| 2.7908 | 2.4 | 3000 | 2.7829 |
| 2.789 | 2.8 | 3500 | 2.7933 |
| 2.7784 | 3.2 | 4000 | 2.8314 |
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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