Instructions to use alexmorley/hlth-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alexmorley/hlth-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="alexmorley/hlth-1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("alexmorley/hlth-1") model = AutoModelForMaskedLM.from_pretrained("alexmorley/hlth-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
hlth-1
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4049
- Accuracy: 0.8561
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
Framework versions
- Transformers 4.54.0
- Pytorch 2.5.1+cu118
- Datasets 4.0.0
- Tokenizers 0.21.4
- Downloads last month
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Model tree for alexmorley/hlth-1
Base model
google-bert/bert-base-uncased