# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("tanmeh/qa_model")
model = AutoModelForQuestionAnswering.from_pretrained("tanmeh/qa_model")Quick Links
qa_model
This model is a fine-tuned version of xlnet-base-cased on the None dataset.
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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 50 | 3.4426 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
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Model tree for tanmeh/qa_model
Base model
xlnet/xlnet-base-cased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="tanmeh/qa_model")