# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("Silxxor/qa_model")
model = AutoModelForQuestionAnswering.from_pretrained("Silxxor/qa_model")Quick Links
qa_model
This model is a fine-tuned version of DeepPavlov/rubert-base-cased on the sberquad dataset. It achieves the following results on the evaluation set:
- Loss: 4.3776
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: 0.0002
- train_batch_size: 64
- eval_batch_size: 64
- seed: 2023
- 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 | 1 | 4.6658 |
| No log | 2.0 | 2 | 4.3776 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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Model tree for Silxxor/qa_model
Base model
DeepPavlov/rubert-base-cased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Silxxor/qa_model")