
<!-- 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. -->

# xlm-roberta-base_seed42_esp-hau-eng_train

This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0152
- Spearman Corr: 0.8636

## 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: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Spearman Corr |
|:-------------:|:-----:|:-----:|:---------------:|:-------------:|
| No log        | 0.54  | 200   | 0.0275          | 0.7044        |
| No log        | 1.08  | 400   | 0.0237          | 0.7348        |
| No log        | 1.62  | 600   | 0.0258          | 0.7480        |
| 0.0358        | 2.15  | 800   | 0.0260          | 0.7627        |
| 0.0358        | 2.69  | 1000  | 0.0337          | 0.7743        |
| 0.0358        | 3.23  | 1200  | 0.0228          | 0.7731        |
| 0.0358        | 3.77  | 1400  | 0.0191          | 0.7915        |
| 0.0212        | 4.31  | 1600  | 0.0302          | 0.7898        |
| 0.0212        | 4.85  | 1800  | 0.0220          | 0.8003        |
| 0.0212        | 5.38  | 2000  | 0.0221          | 0.8042        |
| 0.0212        | 5.92  | 2200  | 0.0247          | 0.8105        |
| 0.0144        | 6.46  | 2400  | 0.0199          | 0.8130        |
| 0.0144        | 7.0   | 2600  | 0.0203          | 0.8177        |
| 0.0144        | 7.54  | 2800  | 0.0261          | 0.8170        |
| 0.0101        | 8.08  | 3000  | 0.0174          | 0.8251        |
| 0.0101        | 8.61  | 3200  | 0.0221          | 0.8260        |
| 0.0101        | 9.15  | 3400  | 0.0220          | 0.8312        |
| 0.0101        | 9.69  | 3600  | 0.0179          | 0.8316        |
| 0.0075        | 10.23 | 3800  | 0.0178          | 0.8359        |
| 0.0075        | 10.77 | 4000  | 0.0220          | 0.8330        |
| 0.0075        | 11.31 | 4200  | 0.0168          | 0.8409        |
| 0.0075        | 11.84 | 4400  | 0.0184          | 0.8394        |
| 0.0059        | 12.38 | 4600  | 0.0182          | 0.8402        |
| 0.0059        | 12.92 | 4800  | 0.0171          | 0.8390        |
| 0.0059        | 13.46 | 5000  | 0.0161          | 0.8446        |
| 0.0048        | 14.0  | 5200  | 0.0179          | 0.8449        |
| 0.0048        | 14.54 | 5400  | 0.0173          | 0.8456        |
| 0.0048        | 15.07 | 5600  | 0.0162          | 0.8484        |
| 0.0048        | 15.61 | 5800  | 0.0180          | 0.8470        |
| 0.0041        | 16.15 | 6000  | 0.0190          | 0.8500        |
| 0.0041        | 16.69 | 6200  | 0.0167          | 0.8525        |
| 0.0041        | 17.23 | 6400  | 0.0159          | 0.8515        |
| 0.0041        | 17.77 | 6600  | 0.0174          | 0.8526        |
| 0.0036        | 18.3  | 6800  | 0.0175          | 0.8519        |
| 0.0036        | 18.84 | 7000  | 0.0158          | 0.8553        |
| 0.0036        | 19.38 | 7200  | 0.0154          | 0.8551        |
| 0.0036        | 19.92 | 7400  | 0.0159          | 0.8548        |
| 0.0032        | 20.46 | 7600  | 0.0165          | 0.8546        |
| 0.0032        | 21.0  | 7800  | 0.0152          | 0.8570        |
| 0.0032        | 21.53 | 8000  | 0.0163          | 0.8566        |
| 0.0029        | 22.07 | 8200  | 0.0150          | 0.8574        |
| 0.0029        | 22.61 | 8400  | 0.0161          | 0.8574        |
| 0.0029        | 23.15 | 8600  | 0.0163          | 0.8588        |
| 0.0029        | 23.69 | 8800  | 0.0156          | 0.8563        |
| 0.0026        | 24.23 | 9000  | 0.0164          | 0.8594        |
| 0.0026        | 24.76 | 9200  | 0.0175          | 0.8598        |
| 0.0026        | 25.3  | 9400  | 0.0145          | 0.8611        |
| 0.0026        | 25.84 | 9600  | 0.0152          | 0.8623        |
| 0.0024        | 26.38 | 9800  | 0.0162          | 0.8618        |
| 0.0024        | 26.92 | 10000 | 0.0155          | 0.8615        |
| 0.0024        | 27.46 | 10200 | 0.0157          | 0.8615        |
| 0.0023        | 27.99 | 10400 | 0.0158          | 0.8618        |
| 0.0023        | 28.53 | 10600 | 0.0147          | 0.8627        |
| 0.0023        | 29.07 | 10800 | 0.0149          | 0.8632        |
| 0.0023        | 29.61 | 11000 | 0.0152          | 0.8636        |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1

{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "FacebookAI/xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base_seed42_esp-hau-eng_train", "results": []}]}
transformers safetensors xlm-roberta text-classification generated_from_trainer base_model:FacebookAI/xlm-roberta-base license:mit autotrain_compatible endpoints_compatible region:us