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# xlm-roberta-base_seed42_amh-esp-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: 0.0135
- Spearman Corr: 0.8691

## 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        | 0.59  | 200  | 0.0224          | 0.7379        |
| No log        | 1.18  | 400  | 0.0205          | 0.7553        |
| No log        | 1.76  | 600  | 0.0180          | 0.7818        |
| 0.0324        | 2.35  | 800  | 0.0208          | 0.7870        |
| 0.0324        | 2.94  | 1000 | 0.0182          | 0.8010        |
| 0.0324        | 3.53  | 1200 | 0.0172          | 0.7997        |
| 0.0183        | 4.12  | 1400 | 0.0219          | 0.8139        |
| 0.0183        | 4.71  | 1600 | 0.0158          | 0.8185        |
| 0.0183        | 5.29  | 1800 | 0.0165          | 0.8202        |
| 0.0183        | 5.88  | 2000 | 0.0158          | 0.8289        |
| 0.0125        | 6.47  | 2200 | 0.0213          | 0.8286        |
| 0.0125        | 7.06  | 2400 | 0.0163          | 0.8298        |
| 0.0125        | 7.65  | 2600 | 0.0187          | 0.8337        |
| 0.0088        | 8.24  | 2800 | 0.0162          | 0.8387        |
| 0.0088        | 8.82  | 3000 | 0.0155          | 0.8397        |
| 0.0088        | 9.41  | 3200 | 0.0155          | 0.8421        |
| 0.0065        | 10.0  | 3400 | 0.0172          | 0.8443        |
| 0.0065        | 10.59 | 3600 | 0.0167          | 0.8486        |
| 0.0065        | 11.18 | 3800 | 0.0147          | 0.8501        |
| 0.0065        | 11.76 | 4000 | 0.0145          | 0.8515        |
| 0.005         | 12.35 | 4200 | 0.0144          | 0.8548        |
| 0.005         | 12.94 | 4400 | 0.0158          | 0.8554        |
| 0.005         | 13.53 | 4600 | 0.0176          | 0.8516        |
| 0.0041        | 14.12 | 4800 | 0.0182          | 0.8549        |
| 0.0041        | 14.71 | 5000 | 0.0138          | 0.8580        |
| 0.0041        | 15.29 | 5200 | 0.0154          | 0.8572        |
| 0.0041        | 15.88 | 5400 | 0.0150          | 0.8600        |
| 0.0036        | 16.47 | 5600 | 0.0134          | 0.8612        |
| 0.0036        | 17.06 | 5800 | 0.0138          | 0.8628        |
| 0.0036        | 17.65 | 6000 | 0.0146          | 0.8619        |
| 0.0031        | 18.24 | 6200 | 0.0134          | 0.8656        |
| 0.0031        | 18.82 | 6400 | 0.0142          | 0.8641        |
| 0.0031        | 19.41 | 6600 | 0.0130          | 0.8648        |
| 0.0028        | 20.0  | 6800 | 0.0131          | 0.8648        |
| 0.0028        | 20.59 | 7000 | 0.0142          | 0.8660        |
| 0.0028        | 21.18 | 7200 | 0.0149          | 0.8637        |
| 0.0028        | 21.76 | 7400 | 0.0138          | 0.8662        |
| 0.0025        | 22.35 | 7600 | 0.0135          | 0.8661        |
| 0.0025        | 22.94 | 7800 | 0.0130          | 0.8673        |
| 0.0025        | 23.53 | 8000 | 0.0128          | 0.8652        |
| 0.0023        | 24.12 | 8200 | 0.0133          | 0.8677        |
| 0.0023        | 24.71 | 8400 | 0.0134          | 0.8669        |
| 0.0023        | 25.29 | 8600 | 0.0131          | 0.8679        |
| 0.0023        | 25.88 | 8800 | 0.0128          | 0.8687        |
| 0.0021        | 26.47 | 9000 | 0.0139          | 0.8692        |
| 0.0021        | 27.06 | 9200 | 0.0133          | 0.8690        |
| 0.0021        | 27.65 | 9400 | 0.0133          | 0.8686        |
| 0.002         | 28.24 | 9600 | 0.0135          | 0.8691        |


### 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_amh-esp-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