Instructions to use SalmonAI123/BestClean-ViMrclarge-TFIDFPNS-mibadata with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SalmonAI123/BestClean-ViMrclarge-TFIDFPNS-mibadata with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="SalmonAI123/BestClean-ViMrclarge-TFIDFPNS-mibadata")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("SalmonAI123/BestClean-ViMrclarge-TFIDFPNS-mibadata") model = AutoModelForQuestionAnswering.from_pretrained("SalmonAI123/BestClean-ViMrclarge-TFIDFPNS-mibadata", device_map="auto") - Notebooks
- Google Colab
- Kaggle
BestClean-ViMrclarge-TFIDFPNS-mibadata
This model is a fine-tuned version of nguyenvulebinh/vi-mrc-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.4694
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 30 | 1.5771 |
| No log | 2.0 | 60 | 1.4694 |
Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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Model tree for SalmonAI123/BestClean-ViMrclarge-TFIDFPNS-mibadata
Base model
nguyenvulebinh/vi-mrc-large