metadata
license: apache-2.0
language: en
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: distilroberta-base-squad_v2
results: []
distilroberta-base-squad_v2
This model is a fine-tuned version of distilroberta-base on the squad_v2 dataset.
Model description
This model is fine-tuned on the extractive question answering task -- The Stanford Question Answering Dataset -- SQuAD2.0.
For convenience this model is prepared to be used with the frameworks PyTorch, Tensorflow and ONNX.
Intended uses & limitations
This model can handle mismatched question-context pairs. Make sure to specify handle_impossible_answer=True when using QuestionAnsweringPipeline.
Example usage:_
>>> from transformers import AutoModelForQuestionAnswering, AutoTokenizer
>>> model = AutoModelForQuestionAnswering.from_pretrained("squirro/distilroberta-base-squad_v2")
>>> tokenizer = AutoTokenizer.from_pretrained("squirro/distilroberta-base-squad_v2")
>>> qa_model = QuestionAnsweringPipeline(model, tokenizer)
>>> qa_model(
>>> question="What's your name?",
>>> context="My name is Clara and I live in Berkeley.",
>>> handle_impossible_answer=True # important!
>>> )
{'score': 0.9027367830276489, 'start': 11, 'end': 16, 'answer': 'Clara'}
Training and evaluation data
Training and evaluation was done on SQuAD2.0.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- distributed_type: tpu
- num_devices: 8
- total_train_batch_size: 512
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Metric | Value |
|---|---|
| epoch | 3 |
| eval_HasAns_exact | 67.5776 |
| eval_HasAns_f1 | 74.3594 |
| eval_HasAns_total | 5928 |
| eval_NoAns_exact | 62.91 |
| eval_NoAns_f1 | 62.91 |
| eval_NoAns_total | 5945 |
| eval_best_exact | 65.2489 |
| eval_best_exact_thresh | 0 |
| eval_best_f1 | 68.6349 |
| eval_best_f1_thresh | 0 |
| eval_exact | 65.2405 |
| eval_f1 | 68.6265 |
| eval_samples | 12165 |
| eval_total | 11873 |
| train_loss | 1.40336 |
| train_runtime | 1365.28 |
| train_samples | 131823 |
| train_samples_per_second | 289.662 |
| train_steps_per_second | 0.567 |
Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6