# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("mcurmei/flat_N_max")
model = AutoModelForQuestionAnswering.from_pretrained("mcurmei/flat_N_max")Quick Links
flat_N_max
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.8536
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.2462 | 1.0 | 2213 | 1.7958 |
| 0.9293 | 2.0 | 4426 | 1.8093 |
| 0.7249 | 3.0 | 6639 | 1.8536 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.0
- Tokenizers 0.12.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="mcurmei/flat_N_max")