# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("eisenjulian/viz-wiz-bert-base-uncased_f32")
model = AutoModelForMaskedLM.from_pretrained("eisenjulian/viz-wiz-bert-base-uncased_f32")Quick Links
viz-wiz-bert-base-uncased_f32
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0723
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: 2e-05
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 321 | 1.1645 |
| 1.344 | 2.0 | 642 | 1.0789 |
| 1.344 | 3.0 | 963 | 1.0537 |
| 1.1234 | 4.0 | 1284 | 1.0195 |
| 1.065 | 5.0 | 1605 | 1.0723 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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Model tree for eisenjulian/viz-wiz-bert-base-uncased_f32
Base model
google-bert/bert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="eisenjulian/viz-wiz-bert-base-uncased_f32")