Instructions to use muhtasham/small-mlm-wikitext with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use muhtasham/small-mlm-wikitext with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="muhtasham/small-mlm-wikitext")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("muhtasham/small-mlm-wikitext") model = AutoModelForMaskedLM.from_pretrained("muhtasham/small-mlm-wikitext") - Notebooks
- Google Colab
- Kaggle
small-mlm-wikitext
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 |
|---|---|---|---|
| 3.077 | 0.4 | 500 | 2.9034 |
| 2.9927 | 0.8 | 1000 | 2.9247 |
| 2.9484 | 1.2 | 1500 | nan |
| 2.9264 | 1.6 | 2000 | 2.8945 |
| 2.9185 | 2.0 | 2500 | 2.8874 |
| 2.855 | 2.4 | 3000 | 2.9401 |
| 2.8632 | 2.8 | 3500 | 2.9649 |
| 2.8067 | 3.2 | 4000 | 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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