Instructions to use sanjin7/distilbert-base-uncased_proba2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sanjin7/distilbert-base-uncased_proba2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="sanjin7/distilbert-base-uncased_proba2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("sanjin7/distilbert-base-uncased_proba2") model = AutoModelForMaskedLM.from_pretrained("sanjin7/distilbert-base-uncased_proba2") - Notebooks
- Google Colab
- Kaggle
distilbert-base-uncased_proba2
This model is a fine-tuned version of sanjin7/distilbert-base-uncased_proba on an unknown dataset.
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: 20
Framework versions
- Transformers 4.25.1
- Pytorch 1.14.0.dev20221202
- Datasets 2.7.1
- Tokenizers 0.13.2
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