# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("rsml/bbert_qa")
model = AutoModelForQuestionAnswering.from_pretrained("rsml/bbert_qa")Quick Links
bbert_qa
This model is a fine-tuned version of 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
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="rsml/bbert_qa")