base_model: mrm8488/longformer-base-4096-finetuned-squadv2
tags:
- generated_from_trainer
license: apache-2.0
datasets:
- Kkordik/NovelQSI
language:
- en
model-index:
- name: Kkordik/test_longformer_4096_qsi
results:
- task:
type: question-answering
dataset:
type: Kkordik/NovelQSI
name: NovelQSI
split: test
metrics:
- type: exact_match
value: 20.346
verified: false
- type: f1
value: 26.58
verified: false
longformer_4096_qsi
This model is a fine-tuned version of mrm8488/longformer-base-4096-finetuned-squadv2 on a tiny NovelQSI dataset. It achieves the following results on the evaluation set:
- Loss: 2.9598
Model description
This model is a test model for my research project. The idea of the model is to understand which novel character said the requested quote. It achieves a bit better results on the ´test´ split of the NovelQSI dataset than base longformer-base-4096-finetuned-squadv2 model on the same dataset split.
Base model results:
{
"exact_match": {
"confidence_interval": [8.754452551305853, 14.718614718614718],
"score": 12.121212121212121,
"standard_error": 1.8579217243778676
},
"f1": {
"confidence_interval": [18.469101076147584, 28.28409063313956],
"score": 22.799422799422796,
"standard_error": 2.896728175757627
},
"latency_in_seconds": 0.7730605573419919,
"samples_per_second": 1.2935597224598967,
"total_time_in_seconds": 178.5769887460001
}
Achieved results:
{
"exact_match": {
"confidence_interval": [16.017316017316016, 24.242424242424242],
"score": 20.346320346320347,
"standard_error": 2.9434375492784994
},
"f1": {
"confidence_interval": [23.123469058324783, 31.823648733317036],
"score": 26.580086580086572,
"standard_error": 2.593030474995015
},
"latency_in_seconds": 0.8093855569913422,
"samples_per_second": 1.235505120349827,
"total_time_in_seconds": 186.96806366500005
}
The results have shown, that the technique has its future.
Training and evaluation data
You can find training code in the github repo of my research:
https://github.com/Kkordik/NovelQSI
It was trained and evaluated in notebooks, so it is easy to reproduce.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 93 | 3.0886 |
| No log | 1.99 | 186 | 3.3755 |
| No log | 2.99 | 279 | 2.9598 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0