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xlmr-en-de-train_shuffled-1986-test2000
patpizio/xlmr-en-de-train_shuffled-1986-test2000
xlmr-en-de-train_shuffled-1986-test2000 is a text classification model from patpizio. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- xlm-roberta
- Access
- Public
- Created
- Nov 23, 2023
- Updated
- Nov 23, 2023
- SHA
- f157168d
Try a prompt
Base models
Datasets
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. -->
xlmr-en-de-train_shuffled-1986-test2000
This model is a fine-tuned version of xlm-roberta-base on the wmt20_mlqe_task1 dataset. It achieves the following results on the evaluation set:
- Loss: 0.5216
- R Squared: 0.0640
- Mae: 0.5363
- Pearson R: 0.4009
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: 16
- eval_batch_size: 16
- seed: 1986
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | R Squared | Mae | Pearson R |
|---|---|---|---|---|---|---|
| No log | 1.0 | 375 | 0.5588 | -0.0028 | 0.5813 | 0.3172 |
| 0.6965 | 2.0 | 750 | 0.5465 | 0.0193 | 0.5548 | 0.3819 |
| 0.6808 | 3.0 | 1125 | 0.5216 | 0.0640 | 0.5363 | 0.4009 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.6
- Tokenizers 0.14.1
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Nov 23, 2023
- Updated
- Nov 23, 2023