Instructions to use rsml/bbert_qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rsml/bbert_qa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="rsml/bbert_qa")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("rsml/bbert_qa") model = AutoModelForQuestionAnswering.from_pretrained("rsml/bbert_qa") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - squad | |
| model-index: | |
| - name: bbert_qa | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # bbert_qa | |
| This model is a fine-tuned version of [bionlp/bluebert_pubmed_uncased_L-12_H-768_A-12](https://huggingface.co/bionlp/bluebert_pubmed_uncased_L-12_H-768_A-12) on the squad dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.6818 | |
| ## 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 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | No log | 1.0 | 250 | 2.3490 | | |
| | 2.7154 | 2.0 | 500 | 1.7686 | | |
| | 2.7154 | 3.0 | 750 | 1.6818 | | |
| ### Framework versions | |
| - Transformers 4.30.2 | |
| - Pytorch 2.0.1+cu118 | |
| - Datasets 2.13.1 | |
| - Tokenizers 0.13.3 | |