metadata
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: xlm-roberta-base-finetuned-code-mixed-DS
results: []
xlm-roberta-base-finetuned-code-mixed-DS
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8266
- Accuracy: 0.6318
- Precision: 0.5781
- Recall: 0.5978
- F1: 0.5677
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: 4.932923543227153e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 1.0602 | 1.0 | 248 | 1.0280 | 0.5211 | 0.4095 | 0.4557 | 0.3912 |
| 0.9741 | 1.99 | 496 | 0.9318 | 0.5533 | 0.4758 | 0.5002 | 0.4415 |
| 0.8585 | 2.99 | 744 | 0.8585 | 0.6076 | 0.5539 | 0.5731 | 0.5353 |
| 0.7293 | 3.98 | 992 | 0.8266 | 0.6318 | 0.5781 | 0.5978 | 0.5677 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1