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# xlm-roberta-base-finetuning-wrime-random4000-epoch6-test02

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3140
- Jaccard (sample): [0.5962264150943396, 0.37719298245614036, 0.6033057851239669, 0.26262626262626265, 0.0, 0.12955465587044535, 0.17261904761904762, 0.0]
- Jaccard (macro): 0.2677
- Macro-precision: 0.4464
- Macro-recall: 0.3365
- Macro F1: 0.3734
- Micro F1: 0.5828
- Accuracy: 0.3408

## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 6
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Jaccard (sample)                                                                                                                       | Jaccard (macro) | Macro-precision | Macro-recall | Macro F1 | Micro F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------:|:---------------:|:---------------:|:------------:|:--------:|:--------:|:--------:|
| No log        | 1.0   | 125  | 0.4217          | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]                                                                                               | 0.0             | 0.0             | 0.0          | 0.0      | 0.0      | 0.0775   |
| No log        | 2.0   | 250  | 0.3666          | [0.35344827586206895, 0.2838709677419355, 0.19130434782608696, 0.0, 0.0, 0.0, 0.024390243902439025, 0.0]                               | 0.1066          | 0.3768          | 0.1228       | 0.1667   | 0.3094   | 0.1608   |
| No log        | 3.0   | 375  | 0.3491          | [0.5525876460767947, 0.2861635220125786, 0.4694244604316547, 0.0, 0.0, 0.061946902654867256, 0.07971014492753623, 0.0]                 | 0.1812          | 0.3449          | 0.2430       | 0.2575   | 0.4968   | 0.2292   |
| 0.4326        | 4.0   | 500  | 0.3245          | [0.6029411764705882, 0.3519061583577713, 0.5591836734693878, 0.06746031746031746, 0.0, 0.15637860082304528, 0.19101123595505617, 0.0]  | 0.2411          | 0.4551          | 0.3072       | 0.3385   | 0.5553   | 0.3142   |
| 0.4326        | 5.0   | 625  | 0.3166          | [0.6094032549728752, 0.3373134328358209, 0.5962343096234309, 0.23508771929824562, 0.0, 0.10970464135021098, 0.16149068322981366, 0.0]  | 0.2562          | 0.4511          | 0.3201       | 0.3582   | 0.5761   | 0.3342   |
| 0.4326        | 6.0   | 750  | 0.3140          | [0.5962264150943396, 0.37719298245614036, 0.6033057851239669, 0.26262626262626265, 0.0, 0.12955465587044535, 0.17261904761904762, 0.0] | 0.2677          | 0.4464          | 0.3365       | 0.3734   | 0.5828   | 0.3408   |


### Framework versions

- Transformers 4.35.2
- Pytorch 1.13.0
- Datasets 2.15.0
- Tokenizers 0.15.0

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-finetuning-wrime-random4000-epoch6-test02", "results": []}]}
transformers tensorboard safetensors xlm-roberta text-classification generated_from_trainer base_model:xlm-roberta-base license:mit autotrain_compatible endpoints_compatible region:us