# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("rooftopcoder/bert-base-uncased-coqa")
model = AutoModelForQuestionAnswering.from_pretrained("rooftopcoder/bert-base-uncased-coqa")Quick Links
bert-base-uncased-coqa
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.8077
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: 64
- 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 |
|---|---|---|---|
| 3.0963 | 1.0 | 3396 | 2.8237 |
| 2.7925 | 2.0 | 6792 | 2.8077 |
| 2.7639 | 3.0 | 10188 | 2.8077 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.3
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Model tree for rooftopcoder/bert-base-uncased-coqa
Base model
google-bert/bert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="rooftopcoder/bert-base-uncased-coqa")