Instructions to use nova-sqoin/hotel_qa_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nova-sqoin/hotel_qa_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="nova-sqoin/hotel_qa_model")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("nova-sqoin/hotel_qa_model") model = AutoModelForQuestionAnswering.from_pretrained("nova-sqoin/hotel_qa_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
sqoin_qa_model_first
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 6.0349
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 | 1 | 6.1184 |
| No log | 2.0 | 2 | 6.0692 |
| No log | 3.0 | 3 | 6.0349 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
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Model tree for nova-sqoin/hotel_qa_model
Base model
google-bert/bert-base-cased