---
tags:
- generated_from_trainer
datasets:
- uonlp/CulturaX
metrics:
- accuracy
model-index:
- name: gpt2+ts_cx-cs_00000-00019_50k
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: uonlp/CulturaX cs
      type: uonlp/CulturaX
      args: cs
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.39971894120768026
---

<!-- 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. -->

# gpt2+ts_cx-cs_00000-00019_50k

This model is a fine-tuned version of [](https://huggingface.co/) on the uonlp/CulturaX cs dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4399
- Accuracy: 0.3997

## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 4.6338        | 0.04  | 10000  | 4.5133          | 0.2968   |
| 4.2588        | 0.07  | 20000  | 4.1531          | 0.3284   |
| 4.0955        | 0.11  | 30000  | 3.9906          | 0.3432   |
| 3.9884        | 0.15  | 40000  | 3.8866          | 0.3530   |
| 3.914         | 0.18  | 50000  | 3.8144          | 0.3601   |
| 3.8563        | 0.22  | 60000  | 3.7592          | 0.3656   |
| 3.8136        | 0.25  | 70000  | 3.7137          | 0.3701   |
| 3.7762        | 0.29  | 80000  | 3.6766          | 0.3740   |
| 3.7481        | 0.33  | 90000  | 3.6468          | 0.3773   |
| 3.7199        | 0.36  | 100000 | 3.6194          | 0.3800   |
| 3.6886        | 0.4   | 110000 | 3.5967          | 0.3824   |
| 3.677         | 0.44  | 120000 | 3.5789          | 0.3843   |
| 3.6611        | 0.47  | 130000 | 3.5600          | 0.3863   |
| 3.6442        | 0.51  | 140000 | 3.5443          | 0.3879   |
| 3.6285        | 0.55  | 150000 | 3.5313          | 0.3894   |
| 3.6126        | 0.58  | 160000 | 3.5176          | 0.3910   |
| 3.6051        | 0.62  | 170000 | 3.5063          | 0.3921   |
| 3.5946        | 0.65  | 180000 | 3.4957          | 0.3933   |
| 3.5883        | 0.69  | 190000 | 3.4858          | 0.3944   |
| 3.5789        | 0.73  | 200000 | 3.4788          | 0.3951   |
| 3.5693        | 0.76  | 210000 | 3.4702          | 0.3963   |
| 3.5584        | 0.8   | 220000 | 3.4632          | 0.3970   |
| 3.5546        | 0.84  | 230000 | 3.4574          | 0.3977   |
| 3.5434        | 0.87  | 240000 | 3.4520          | 0.3983   |
| 3.5447        | 0.91  | 250000 | 3.4473          | 0.3988   |
| 3.5353        | 0.95  | 260000 | 3.4427          | 0.3993   |
| 3.5382        | 0.98  | 270000 | 3.4402          | 0.3997   |


### Framework versions

- Transformers 4.37.1
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1