# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("damapika/roberta-base_mod_quoref")
model = AutoModelForQuestionAnswering.from_pretrained("damapika/roberta-base_mod_quoref", device_map="auto")Quick Links
roberta-base_mod_quoref
This model is a fine-tuned version of damapika/roberta-base_mod_squad on the quoref dataset. It achieves the following results on the evaluation set:
- Loss: 1.5566
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: 3e-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 |
|---|---|---|---|
| 1.1263 | 1.0 | 1213 | 1.2665 |
| 0.7404 | 2.0 | 2426 | 1.3567 |
| 0.5172 | 3.0 | 3639 | 1.5566 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
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# 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="damapika/roberta-base_mod_quoref")