---
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: roberta-finetuned-squad_v2
  results:
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: SQuAD v2
      type: squad_v2
      split: validation
    metrics:
    - type: exact
      value: 100.0
      name: Exact
    - type: f1
      value: 100.0
      name: F1
    - type: total
      value: 2
      name: Total
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# roberta-finetuned-squad_v2

This model was trained from scratch on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8582

## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.9129        | 0.2   | 100  | 1.4700          |
| 1.4395        | 0.39  | 200  | 1.2407          |
| 1.2356        | 0.59  | 300  | 1.0325          |
| 1.1284        | 0.78  | 400  | 0.9750          |
| 1.0821        | 0.98  | 500  | 0.9345          |
| 0.9978        | 1.18  | 600  | 0.9893          |
| 0.9697        | 1.37  | 700  | 0.9300          |
| 0.9455        | 1.57  | 800  | 0.9351          |
| 0.9322        | 1.76  | 900  | 0.9451          |
| 0.9269        | 1.96  | 1000 | 0.9064          |
| 0.9105        | 2.16  | 1100 | 0.8837          |
| 0.8805        | 2.35  | 1200 | 0.8876          |
| 0.8703        | 2.55  | 1300 | 0.9853          |
| 0.8699        | 2.75  | 1400 | 0.9235          |
| 0.8633        | 2.94  | 1500 | 0.8930          |
| 0.828         | 3.14  | 1600 | 0.8582          |
| 0.8284        | 3.33  | 1700 | 0.9203          |
| 0.8076        | 3.53  | 1800 | 0.8866          |
| 0.7805        | 3.73  | 1900 | 0.9099          |
| 0.7974        | 3.92  | 2000 | 0.8746          |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1