---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: XLMRoBERTa_Lexical_Dataset45K
  results: []
---

<!-- 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. -->

# XLMRoBERTa_Lexical_Dataset45K

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: 0.4964
- Accuracy: 0.8870
- F1: 0.8870

## 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: 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: 15

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|:------:|
| No log        | 0.2841  | 200   | 0.4614          | 0.7988   | 0.7972 |
| No log        | 0.5682  | 400   | 0.3716          | 0.8339   | 0.8335 |
| No log        | 0.8523  | 600   | 0.3603          | 0.8456   | 0.8474 |
| 0.455         | 1.1364  | 800   | 0.3298          | 0.8618   | 0.8612 |
| 0.455         | 1.4205  | 1000  | 0.3466          | 0.8621   | 0.8598 |
| 0.455         | 1.7045  | 1200  | 0.3051          | 0.8697   | 0.8712 |
| 0.455         | 1.9886  | 1400  | 0.2924          | 0.8773   | 0.8774 |
| 0.3111        | 2.2727  | 1600  | 0.2764          | 0.8847   | 0.8853 |
| 0.3111        | 2.5568  | 1800  | 0.2648          | 0.8865   | 0.8866 |
| 0.3111        | 2.8409  | 2000  | 0.2571          | 0.8890   | 0.8894 |
| 0.2536        | 3.125   | 2200  | 0.2745          | 0.8901   | 0.8898 |
| 0.2536        | 3.4091  | 2400  | 0.2801          | 0.8729   | 0.8747 |
| 0.2536        | 3.6932  | 2600  | 0.2863          | 0.8892   | 0.8885 |
| 0.2536        | 3.9773  | 2800  | 0.2587          | 0.8909   | 0.8902 |
| 0.2249        | 4.2614  | 3000  | 0.2728          | 0.8905   | 0.8903 |
| 0.2249        | 4.5455  | 3200  | 0.2782          | 0.8873   | 0.8882 |
| 0.2249        | 4.8295  | 3400  | 0.2696          | 0.8900   | 0.8908 |
| 0.1984        | 5.1136  | 3600  | 0.3226          | 0.8896   | 0.8890 |
| 0.1984        | 5.3977  | 3800  | 0.2821          | 0.8921   | 0.8923 |
| 0.1984        | 5.6818  | 4000  | 0.3206          | 0.8818   | 0.8830 |
| 0.1984        | 5.9659  | 4200  | 0.2800          | 0.8938   | 0.8936 |
| 0.178         | 6.25    | 4400  | 0.2892          | 0.8938   | 0.8938 |
| 0.178         | 6.5341  | 4600  | 0.3262          | 0.8875   | 0.8880 |
| 0.178         | 6.8182  | 4800  | 0.3199          | 0.8876   | 0.8874 |
| 0.1556        | 7.1023  | 5000  | 0.3237          | 0.8889   | 0.8892 |
| 0.1556        | 7.3864  | 5200  | 0.3540          | 0.8883   | 0.8880 |
| 0.1556        | 7.6705  | 5400  | 0.3361          | 0.8925   | 0.8925 |
| 0.1556        | 7.9545  | 5600  | 0.3369          | 0.8868   | 0.8877 |
| 0.1344        | 8.2386  | 5800  | 0.3449          | 0.8889   | 0.8887 |
| 0.1344        | 8.5227  | 6000  | 0.3813          | 0.8888   | 0.8884 |
| 0.1344        | 8.8068  | 6200  | 0.3463          | 0.8908   | 0.8910 |
| 0.1203        | 9.0909  | 6400  | 0.3781          | 0.8886   | 0.8893 |
| 0.1203        | 9.375   | 6600  | 0.3825          | 0.8897   | 0.8897 |
| 0.1203        | 9.6591  | 6800  | 0.3842          | 0.8890   | 0.8895 |
| 0.1203        | 9.9432  | 7000  | 0.3894          | 0.8885   | 0.8893 |
| 0.1069        | 10.2273 | 7200  | 0.4063          | 0.8903   | 0.8900 |
| 0.1069        | 10.5114 | 7400  | 0.3976          | 0.8908   | 0.8912 |
| 0.1069        | 10.7955 | 7600  | 0.4013          | 0.8892   | 0.8893 |
| 0.0942        | 11.0795 | 7800  | 0.4663          | 0.8888   | 0.8886 |
| 0.0942        | 11.3636 | 8000  | 0.4394          | 0.8867   | 0.8871 |
| 0.0942        | 11.6477 | 8200  | 0.4770          | 0.8852   | 0.8859 |
| 0.0942        | 11.9318 | 8400  | 0.4278          | 0.8881   | 0.8881 |
| 0.0827        | 12.2159 | 8600  | 0.4431          | 0.8874   | 0.8877 |
| 0.0827        | 12.5    | 8800  | 0.4553          | 0.8851   | 0.8856 |
| 0.0827        | 12.7841 | 9000  | 0.4546          | 0.8859   | 0.8861 |
| 0.0772        | 13.0682 | 9200  | 0.4571          | 0.8883   | 0.8881 |
| 0.0772        | 13.3523 | 9400  | 0.4655          | 0.8872   | 0.8870 |
| 0.0772        | 13.6364 | 9600  | 0.4700          | 0.8872   | 0.8876 |
| 0.0772        | 13.9205 | 9800  | 0.4817          | 0.8863   | 0.8867 |
| 0.0692        | 14.2045 | 10000 | 0.4838          | 0.8864   | 0.8868 |
| 0.0692        | 14.4886 | 10200 | 0.4825          | 0.8868   | 0.8871 |
| 0.0692        | 14.7727 | 10400 | 0.4964          | 0.8870   | 0.8870 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1