Instructions to use Norphel/dzoQA_tibet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Norphel/dzoQA_tibet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Norphel/dzoQA_tibet")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Norphel/dzoQA_tibet") model = AutoModelForQuestionAnswering.from_pretrained("Norphel/dzoQA_tibet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
dzoQA_tibet
This model is a fine-tuned version of sangjeedondrub/tibetan-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 4.7768
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 |
|---|---|---|---|
| No log | 1.0 | 17 | 5.1251 |
| No log | 2.0 | 34 | 4.8563 |
| No log | 3.0 | 51 | 4.7984 |
| No log | 4.0 | 68 | 4.7833 |
| No log | 5.0 | 85 | 4.7768 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
- Downloads last month
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Model tree for Norphel/dzoQA_tibet
Base model
sangjeedondrub/tibetan-roberta-base