---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: gitlab-mr-analysis-default-model
  results: []
---

<!-- 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. -->

# gitlab-mr-analysis-default-model

This model is a fine-tuned version of [distilbert-base-uncased-distilled-squad](https://huggingface.co/distilbert-base-uncased-distilled-squad) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3356
- Accuracy: 94.72759226713534%
- F1: 0.9612

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy           | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------------------:|:------:|
| No log        | 1.0   | 284  | 0.4056          | 85.76449912126537% | 0.7346 |
| 0.4789        | 2.0   | 568  | 0.2722          | 91.47627416520211% | 0.9335 |
| 0.4789        | 3.0   | 852  | 0.2413          | 94.37609841827768% | 0.9592 |
| 0.1185        | 4.0   | 1137 | 0.2776          | 94.37609841827768% | 0.9574 |
| 0.1185        | 5.0   | 1421 | 0.3132          | 93.84885764499121% | 0.9472 |
| 0.0378        | 6.0   | 1705 | 0.3323          | 94.28822495606327% | 0.9582 |
| 0.0378        | 7.0   | 1989 | 0.3393          | 94.28822495606327% | 0.9575 |
| 0.0123        | 8.0   | 2274 | 0.3363          | 94.5518453427065%  | 0.9607 |
| 0.0077        | 9.0   | 2558 | 0.3333          | 94.63971880492092% | 0.9606 |
| 0.0077        | 9.99  | 2840 | 0.3356          | 94.72759226713534% | 0.9612 |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.2