Instructions to use dantedgp/distilbert-base-uncased-finetuned-med with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dantedgp/distilbert-base-uncased-finetuned-med with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dantedgp/distilbert-base-uncased-finetuned-med", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dantedgp/distilbert-base-uncased-finetuned-med") model = AutoModelForMaskedLM.from_pretrained("dantedgp/distilbert-base-uncased-finetuned-med", device_map="auto") - Notebooks
- Google Colab
- Kaggle
distilbert-base-uncased-finetuned-med
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.7464
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.0297 | 1.0 | 625 | 1.8281 |
| 1.9024 | 2.0 | 1250 | 1.7538 |
| 1.8602 | 3.0 | 1875 | 1.7567 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for dantedgp/distilbert-base-uncased-finetuned-med
Base model
distilbert/distilbert-base-uncased