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xlm-roberta-en-ru-emoji-v2
amazon-sagemaker-community/xlm-roberta-en-ru-emoji-v2
xlm-roberta-en-ru-emoji-v2 is a text classification model from amazon-sagemaker-community. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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Model card
From the Hugging Face model README
<!-- 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 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
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- Created
- Mar 2, 2022
- Updated
- Sep 25, 2023