Instructions to use Timur1984/sbert_large_nlu_ru-finetuned-squad-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Timur1984/sbert_large_nlu_ru-finetuned-squad-full with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Timur1984/sbert_large_nlu_ru-finetuned-squad-full")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Timur1984/sbert_large_nlu_ru-finetuned-squad-full") model = AutoModelForQuestionAnswering.from_pretrained("Timur1984/sbert_large_nlu_ru-finetuned-squad-full", device_map="auto") - Notebooks
- Google Colab
- Kaggle
sbert_large_nlu_ru-finetuned-squad-full
This model is a fine-tuned version of ruselkomp/sbert_large_nlu_ru-finetuned-squad-full on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6119
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: 6
- eval_batch_size: 6
- 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 | 17 | 0.5747 |
| No log | 2.0 | 34 | 0.6119 |
Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 2.0.1.dev0
- Tokenizers 0.11.6
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