
<!-- 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. -->

# scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8546
- Accuracy: 0.8443
- F1: 0.8187

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 500

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.6138        | 0.27  | 5000  | 0.6955          | 0.8197   | 0.7723 |
| 0.4705        | 0.53  | 10000 | 0.6878          | 0.8280   | 0.7855 |
| 0.4019        | 0.8   | 15000 | 0.6850          | 0.8368   | 0.8005 |
| 0.2929        | 1.07  | 20000 | 0.7296          | 0.8316   | 0.7965 |
| 0.3011        | 1.34  | 25000 | 0.7140          | 0.8426   | 0.8139 |
| 0.2921        | 1.6   | 30000 | 0.7252          | 0.8418   | 0.8152 |
| 0.2799        | 1.87  | 35000 | 0.7186          | 0.8431   | 0.8152 |
| 0.2103        | 2.14  | 40000 | 0.7557          | 0.8462   | 0.8172 |
| 0.2251        | 2.41  | 45000 | 0.7926          | 0.8411   | 0.8095 |
| 0.2118        | 2.67  | 50000 | 0.7915          | 0.8427   | 0.8126 |
| 0.2239        | 2.94  | 55000 | 0.7813          | 0.8416   | 0.8076 |
| 0.1727        | 3.21  | 60000 | 0.8273          | 0.8471   | 0.8224 |
| 0.1785        | 3.47  | 65000 | 0.8192          | 0.8447   | 0.8149 |
| 0.2008        | 3.74  | 70000 | 0.8043          | 0.8464   | 0.8213 |
| 0.1773        | 4.01  | 75000 | 0.8555          | 0.8407   | 0.8121 |
| 0.165         | 4.28  | 80000 | 0.8556          | 0.8456   | 0.8218 |
| 0.1658        | 4.54  | 85000 | 0.8546          | 0.8443   | 0.8187 |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.16.0
- Tokenizers 0.15.0

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["massive"], "metrics": ["accuracy", "f1"], "base_model": "xlm-roberta-base", "model-index": [{"name": "scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "massive", "type": "massive", "config": "all_1.1", "split": "validation", "args": "all_1.1"}, "metrics": [{"type": "accuracy", "value": 0.8442714442468501, "name": "Accuracy"}, {"type": "f1", "value": 0.8187475356490956, "name": "F1"}]}]}]}
transformers pytorch safetensors xlm-roberta text-classification generated_from_trainer dataset:massive base_model:xlm-roberta-base license:mit model-index autotrain_compatible endpoints_compatible region:us