|
--- |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
base_model: DeepPavlov/xlm-roberta-large-en-ru |
|
model-index: |
|
- name: xlm-roberta-en-ru-emoji-v2 |
|
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. --> |
|
|
|
# xlm-roberta-en-ru-emoji-v2 |
|
|
|
This model is a fine-tuned version of [DeepPavlov/xlm-roberta-large-en-ru](https://huggingface.co./DeepPavlov/xlm-roberta-large-en-ru) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 2.3356 |
|
- Accuracy: 0.3102 |
|
|
|
## 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: 96 |
|
- eval_batch_size: 96 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_steps: 500 |
|
- num_epochs: 5 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| No log | 0.4 | 200 | 3.0592 | 0.1204 | |
|
| No log | 0.81 | 400 | 2.5356 | 0.2480 | |
|
| 2.6294 | 1.21 | 600 | 2.4570 | 0.2569 | |
|
| 2.6294 | 1.62 | 800 | 2.3332 | 0.2832 | |
|
| 1.9286 | 2.02 | 1000 | 2.3354 | 0.2803 | |
|
| 1.9286 | 2.42 | 1200 | 2.3610 | 0.2881 | |
|
| 1.9286 | 2.83 | 1400 | 2.3004 | 0.2973 | |
|
| 1.7312 | 3.23 | 1600 | 2.3619 | 0.3026 | |
|
| 1.7312 | 3.64 | 1800 | 2.3596 | 0.3032 | |
|
| 1.5816 | 4.04 | 2000 | 2.2972 | 0.3072 | |
|
| 1.5816 | 4.44 | 2200 | 2.3077 | 0.3073 | |
|
| 1.5816 | 4.85 | 2400 | 2.3356 | 0.3102 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.12.3 |
|
- Pytorch 1.9.1 |
|
- Datasets 1.15.1 |
|
- Tokenizers 0.10.3 |
|
|
