---
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](https://huggingface.co/mrm8488/longformer-base-4096-finetuned-squadv2) on a tiny [NovelQSI](https://huggingface.co/datasets/Kkordik/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