Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use galkowskim/roberta_base_QA_SQUAD_adafactor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use galkowskim/roberta_base_QA_SQUAD_adafactor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="galkowskim/roberta_base_QA_SQUAD_adafactor")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("galkowskim/roberta_base_QA_SQUAD_adafactor") model = AutoModelForSequenceClassification.from_pretrained("galkowskim/roberta_base_QA_SQUAD_adafactor") - Notebooks
- Google Colab
- Kaggle
roberta_base_QA_SQUAD_adafactor
This model is a fine-tuned version of FacebookAI/roberta-base on the arrow 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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1
- Datasets 2.19.0
- Tokenizers 0.19.1
- Downloads last month
- 5
Model tree for galkowskim/roberta_base_QA_SQUAD_adafactor
Base model
FacebookAI/roberta-base