Instructions to use pythonist/distilbert-base-uncased-finetuned-pubmedbykrs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pythonist/distilbert-base-uncased-finetuned-pubmedbykrs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="pythonist/distilbert-base-uncased-finetuned-pubmedbykrs")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("pythonist/distilbert-base-uncased-finetuned-pubmedbykrs") model = AutoModelForQuestionAnswering.from_pretrained("pythonist/distilbert-base-uncased-finetuned-pubmedbykrs") - Notebooks
- Google Colab
- Kaggle
distilbert-base-uncased-finetuned-pubmedbykrs
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: 4.1823
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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 21 | 4.7665 |
| No log | 2.0 | 42 | 4.1823 |
Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
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