# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("suhcrates/yb_model_dir")
model = AutoModelForQuestionAnswering.from_pretrained("suhcrates/yb_model_dir", device_map="auto")Quick Links
yb_model_dir
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset. It achieves the following results on the evaluation set:
- Loss: 1.8316
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 125 | 3.0743 |
| No log | 2.0 | 250 | 2.1030 |
| No log | 3.0 | 375 | 1.8316 |
Framework versions
- Transformers 4.21.3
- Pytorch 1.11.0+cu102
- Datasets 2.8.0
- Tokenizers 0.12.1
- Downloads last month
- 6
# 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="suhcrates/yb_model_dir")