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---
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: albert-base-v2-finetuned-squad
results:
- task:
name: Question Answering
type: question-answering
dataset:
type: squad_v2
name: The Stanford Question Answering Dataset
args: en
metrics:
- type: eval_exact
value: 76.263
- type: eval_f1
value: 84.734
language:
- en
metrics:
- exact_match
- f1
---
<!-- 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. -->
# albert-base-v2-finetuned-squad
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co./albert-base-v2) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4539
- Exact Match: 80.60548722800378
- F1 score: 88.76870326468953
## Model description
This model is fine-tuned on the extractive question answering task -- The Stanford Question Answering Dataset -- SQuAD2.0.
## Intended uses & limitations
More information needed
## 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: 2e-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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.8702 | 1.0 | 5540 | 0.8943 |
| 0.6972 | 2.0 | 11080 | 0.9087 |
| 0.4998 | 3.0 | 16620 | 0.9890 |
| 0.3601 | 4.0 | 22160 | 1.1892 |
| 0.235 | 5.0 | 27700 | 1.4539 |
### Framework versions
- Transformers 4.34.0
- Pytorch 1.12.1
- Datasets 2.14.5
- Tokenizers 0.14.1 |