Instructions to use allistair99/bert-base-uncased-squad-v1-finetuned-SRH-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use allistair99/bert-base-uncased-squad-v1-finetuned-SRH-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="allistair99/bert-base-uncased-squad-v1-finetuned-SRH-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("allistair99/bert-base-uncased-squad-v1-finetuned-SRH-v1") model = AutoModelForQuestionAnswering.from_pretrained("allistair99/bert-base-uncased-squad-v1-finetuned-SRH-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
bert-base-uncased-squad-v1-finetuned-SRH-v1
This model is a fine-tuned version of csarron/bert-base-uncased-squad-v1 on the srh_test66 dataset. It achieves the following results on the evaluation set:
- Loss: 1.6467
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 |
|---|---|---|---|
| 2.4301 | 1.0 | 43 | 1.6138 |
| 1.2414 | 2.0 | 86 | 1.6071 |
| 0.6683 | 3.0 | 129 | 1.6467 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for allistair99/bert-base-uncased-squad-v1-finetuned-SRH-v1
Base model
csarron/bert-base-uncased-squad-v1