# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("lauraparra28/bert-base-cased-finetuned-squad_v2-bs_16")
model = AutoModelForQuestionAnswering.from_pretrained("lauraparra28/bert-base-cased-finetuned-squad_v2-bs_16", device_map="auto")Quick Links
bert-base-cased-finetuned-squad_v2-bs_16
This model is a fine-tuned version of bert-base-cased on the squad_v2 dataset. It achieves the following results on the evaluation set:
- Loss: 1.8737
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.0154 | 1.0 | 8255 | 1.2377 |
| 0.7549 | 2.0 | 16510 | 1.0880 |
| 0.5406 | 3.0 | 24765 | 1.3375 |
| 0.3829 | 4.0 | 33020 | 1.6121 |
| 0.2783 | 5.0 | 41275 | 1.8737 |
Framework versions
- Transformers 4.34.0
- Pytorch 1.12.1
- Datasets 2.14.5
- Tokenizers 0.14.1
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Model tree for lauraparra28/bert-base-cased-finetuned-squad_v2-bs_16
Base model
google-bert/bert-base-cased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="lauraparra28/bert-base-cased-finetuned-squad_v2-bs_16")