Instructions to use tizaino/bert-base-uncased-finetuned-Pisa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tizaino/bert-base-uncased-finetuned-Pisa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="tizaino/bert-base-uncased-finetuned-Pisa")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("tizaino/bert-base-uncased-finetuned-Pisa") model = AutoModelForMaskedLM.from_pretrained("tizaino/bert-base-uncased-finetuned-Pisa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
bert-base-uncased-finetuned-Pisa
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: 1.1132
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 9 | 1.4146 |
| No log | 2.0 | 18 | 1.1013 |
| No log | 3.0 | 27 | 1.1237 |
Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.7.1
- Datasets 1.16.1
- Tokenizers 0.10.3
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