Question Answering
Transformers
PyTorch
TensorBoard
Safetensors
roberta
Generated from Trainer
Eval Results (legacy)
Instructions to use sooolee/roberta-base-finetuned-squad-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sooolee/roberta-base-finetuned-squad-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="sooolee/roberta-base-finetuned-squad-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("sooolee/roberta-base-finetuned-squad-v1") model = AutoModelForQuestionAnswering.from_pretrained("sooolee/roberta-base-finetuned-squad-v1") - Notebooks
- Google Colab
- Kaggle
roberta-base-finetuned-squad-v1
This model is a fine-tuned version of roberta-base on the squad dataset.
Model description
Given a context / content, the model answers to a question by searching the content and extracting the relavant information.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
- training loss: 0.77257
Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.3
- Downloads last month
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Model tree for sooolee/roberta-base-finetuned-squad-v1
Base model
FacebookAI/roberta-baseEvaluation results
- f1 on SQUADself-reported92.296
- exact_match on SQUADself-reported86.045