Instructions to use allistair99/MobileBERT-uncased-squad-v1-BiLSTM-finetuned-SRH-optiparam-unfrozen-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use allistair99/MobileBERT-uncased-squad-v1-BiLSTM-finetuned-SRH-optiparam-unfrozen-v3 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, MobileBertForQuestionAnswering1 tokenizer = AutoTokenizer.from_pretrained("allistair99/MobileBERT-uncased-squad-v1-BiLSTM-finetuned-SRH-optiparam-unfrozen-v3") model = MobileBertForQuestionAnswering1.from_pretrained("allistair99/MobileBERT-uncased-squad-v1-BiLSTM-finetuned-SRH-optiparam-unfrozen-v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MobileBERT-uncased-squad-v1-BiLSTM-finetuned-SRH-optiparam-unfrozen-v3
This model is a fine-tuned version of allistair99/MobileBERT-uncased-squad-v1-BiLSTM-finetuned-squad-optiparam on the srh_test66 dataset. It achieves the following results on the evaluation set:
- Loss: 1.3792
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 | 43 | 1.3137 |
| No log | 2.0 | 86 | 1.3376 |
| No log | 3.0 | 129 | 1.3792 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for allistair99/MobileBERT-uncased-squad-v1-BiLSTM-finetuned-SRH-optiparam-unfrozen-v3
Base model
csarron/mobilebert-uncased-squad-v1